Life_Expectancy_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#import sklearn.preprocessing\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "%matplotlib inline\n",
    "from scipy import linspace, polyval, polyfit, sqrt, stats, randn, optimize\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0  Afghanistan  2015  Developing              65.0            263.0   \n",
       "1  Afghanistan  2014  Developing              59.9            271.0   \n",
       "2  Afghanistan  2013  Developing              59.9            268.0   \n",
       "3  Afghanistan  2012  Developing              59.5            272.0   \n",
       "4  Afghanistan  2011  Developing              59.2            275.0   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "0             62     0.01               71.279624         65.0      1154   \n",
       "1             64     0.01               73.523582         62.0       492   \n",
       "2             66     0.01               73.219243         64.0       430   \n",
       "3             69     0.01               78.184215         67.0      2787   \n",
       "4             71     0.01                7.097109         68.0      3013   \n",
       "\n",
       "     ...      Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
       "0    ...        6.0               8.16         65.0        0.1  584.259210   \n",
       "1    ...       58.0               8.18         62.0        0.1  612.696514   \n",
       "2    ...       62.0               8.13         64.0        0.1  631.744976   \n",
       "3    ...       67.0               8.52         67.0        0.1  669.959000   \n",
       "4    ...       68.0               7.87         68.0        0.1   63.537231   \n",
       "\n",
       "   Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0  33736494.0                   17.2                 17.3   \n",
       "1    327582.0                   17.5                 17.5   \n",
       "2  31731688.0                   17.7                 17.7   \n",
       "3   3696958.0                   17.9                 18.0   \n",
       "4   2978599.0                   18.2                 18.2   \n",
       "\n",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  \n",
       "3                            0.463        9.8  \n",
       "4                            0.454        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv('Life Expectancy Data.csv',sep=',',na_values=['#NAME?'])\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                            0.0\n",
       "Year                               0.0\n",
       "Status                             0.0\n",
       "Life expectancy                    0.0\n",
       "Adult Mortality                    0.0\n",
       "infant deaths                      0.0\n",
       "Alcohol                            0.0\n",
       "percentage expenditure             0.0\n",
       "Hepatitis B                        0.0\n",
       "Measles                            0.0\n",
       " BMI                               0.0\n",
       "under-five deaths                  0.0\n",
       "Polio                              0.0\n",
       "Total expenditure                  0.0\n",
       "Diphtheria                         0.0\n",
       " HIV/AIDS                          0.0\n",
       "GDP                                0.0\n",
       "Population                         0.0\n",
       " thinness  1-19 years              0.0\n",
       " thinness 5-9 years                0.0\n",
       "Income composition of resources    0.0\n",
       "Schooling                          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.duplicated()].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'integer_encoded' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-1bb2b147ebf3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mlabel_encoder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLabelEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0minteger_encoded_1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel_encoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minteger_encoded\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0monehot_encoder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mOneHotEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0minteger_encoded_2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0monehot_encoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'integer_encoded' is not defined"
     ]
    }
   ],
   "source": [
    "#df_country=df['Country']\n",
    "values=df1['Country']\n",
    "label_encoder=LabelEncoder()\n",
    "integer_encoded_1=label_encoder.fit_transform(values)\n",
    "print(integer_encoded)\n",
    "onehot_encoder=OneHotEncoder()\n",
    "integer_encoded_2=onehot_encoder.fit_transform(values)\n",
    "print(integer_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     10\n",
       "Adult Mortality                     10\n",
       "infant deaths                        0\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Hepatitis B                        553\n",
       "Measles                              0\n",
       " BMI                                34\n",
       "under-five deaths                    0\n",
       "Polio                               19\n",
       "Total expenditure                  226\n",
       "Diphtheria                          19\n",
       " HIV/AIDS                            0\n",
       "GDP                                448\n",
       "Population                         652\n",
       " thinness  1-19 years               34\n",
       " thinness 5-9 years                 34\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                            0\n",
       "Year                               0\n",
       "Status                             0\n",
       "Life expectancy                    0\n",
       "Adult Mortality                    0\n",
       "infant deaths                      0\n",
       "Alcohol                            0\n",
       "percentage expenditure             0\n",
       "Hepatitis B                        0\n",
       "Measles                            0\n",
       " BMI                               0\n",
       "under-five deaths                  0\n",
       "Polio                              0\n",
       "Total expenditure                  0\n",
       "Diphtheria                         0\n",
       " HIV/AIDS                          0\n",
       "GDP                                0\n",
       "Population                         0\n",
       " thinness  1-19 years              0\n",
       " thinness 5-9 years                0\n",
       "Income composition of resources    0\n",
       "Schooling                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df1.dropna()\n",
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_outliers_tukey(x):\n",
    "    q1 = x.quantile(.25)\n",
    "    q3 = x.quantile(.75)\n",
    "    iqr = q3 - q1\n",
    "    floor = q1 - 1.5*iqr\n",
    "    ceiling = q3 + 1.5*iqr\n",
    "    outlier_indices = list(x.index[(x < floor) | (x > ceiling)])\n",
    "    outlier_values = list(x[outlier_indices])\n",
    "    return outlier_indices, outlier_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers for Infant death\n",
      "[  54   54   54   54   54   54   54   55   55   55   55   55   55   55\n",
      "   56   56   56   56   57   57   57   57   57   58   58   58   58   58\n",
      "   58   59   59   59   59   60   60   61   61   61   61   62   62   62\n",
      "   62   63   64   64   64   64   65   65   66   66   66   66   66   67\n",
      "   67   68   68   68   68   69   69   69   69   69   70   70   70   71\n",
      "   71   72   72   72   73   74   74   74   75   75   76   76   77   78\n",
      "   78   79   80   80   81   81   82   82   82   84   84   84   85   85\n",
      "   87   87   87   87   88   88   88   88   88   90   93   95   98   99\n",
      "  103  104  104  109  111  111  112  118  119  124  126  129  134  135\n",
      "  138  140  143  144  145  149  150  154  154  156  159  162  163  164\n",
      "  169  171  173  174  174  177  177  182  185  185  186  187  196  201\n",
      "  215  231  248  266  285  307  332  359  360  364  364  365  365  366\n",
      "  367  369  369  371  371  372  391  422  457  490  490  498  505  513\n",
      "  521  527  536  542  549  556  957 1000 1100 1100 1200 1300 1300 1400\n",
      " 1500 1500 1600]\n",
      "Outliers for Alcohol\n",
      "[17.31 17.87]\n",
      "Outliers for Percentage Expenditure\n",
      "[ 1230.748231  1259.940376  1275.689625  1275.877569  1299.159757\n",
      "  1340.553699  1346.246697  1356.022164  1389.772683  1398.042623\n",
      "  1403.022211  1413.73543   1423.802386  1435.487755  1446.529217\n",
      "  1475.030397  1486.236406  1487.896562  1491.703605  1504.329462\n",
      "  1521.672751  1529.497771  1542.635979  1562.520827  1578.872339\n",
      "  1581.511914  1586.724172  1626.591186  1657.328352  1678.392773\n",
      "  1694.467075  1700.841534  1701.195787  1712.225506  1717.088711\n",
      "  1731.755747  1758.822757  1802.017593  1807.071336  1842.463782\n",
      "  1851.713262  1859.095621  1871.736563  1884.098811  1888.149904\n",
      "  1895.342792  1899.107385  1902.693048  1904.00231   1904.12469\n",
      "  1913.356642  1934.398154  1958.525877  1968.816817  2019.812779\n",
      "  2044.169761  2051.748615  2052.393076  2124.921517  2146.945741\n",
      "  2159.756205  2160.380199  2163.043414  2183.106986  2211.744178\n",
      "  2247.035692  2260.777642  2263.103797  2267.252192  2283.891796\n",
      "  2284.58215   2331.532804  2352.999591  2377.010299  2380.699796\n",
      "  2425.403891  2429.854745  2442.222054  2443.325028  2528.992935\n",
      "  2568.237059  2578.888314  2655.573684  2698.01817   2700.07305\n",
      "  2785.139364  2813.985285  2883.334911  2884.020194  2943.375396\n",
      "  3064.301144  3108.288623  3119.349824  3189.754439  3192.887363\n",
      "  3205.019918  3410.284431  3451.185079  3519.258515  3557.455507\n",
      "  3582.848308  3601.287457  3632.243121  3652.869332  3682.88717\n",
      "  3715.370095  3779.655273  3793.161624  3829.550928  3830.184587\n",
      "  3979.057728  4057.637383  4238.540035  4255.781693  4270.914882\n",
      "  4279.895512  4348.33531   4460.390006  4506.255963  4509.234803\n",
      "  4572.441963  4687.845565  4793.904924  4831.644796  4873.8188\n",
      "  5047.254058  5068.214488  5160.508378  5219.668802  5228.821581\n",
      "  5243.316638  5291.234786  5316.877456  5439.691769  5451.701225\n",
      "  5513.329924  5579.199083  5596.535203  5689.991931  5842.374765\n",
      "  5992.588029  6012.92584   6187.062017  6333.177967  6386.95437\n",
      "  6407.535954  6415.35735   6616.695125  6646.953935  6739.677606\n",
      "  7002.785925  7163.348923  7172.275229  7191.051507  7423.228774\n",
      "  7453.8644    7584.078579  7641.270645  7777.556353  7877.33656\n",
      "  7878.372355  7946.743581  8053.557638  8246.130437  8272.306757\n",
      "  8285.264655  8329.731655  8350.193523  8433.936814  8547.292357\n",
      "  8649.674911  8875.786493  9733.228019  9748.636237 10111.38928\n",
      " 10769.36305  10947.02327  10986.26527  11477.6671   11714.99858\n",
      " 11734.85381  12372.05188  15345.4907   15515.75234  16255.16198\n",
      " 17028.52798  18961.3486  ]\n",
      "Outliers for Hepatitis B\n",
      "[ 2.  2.  2.  2.  4.  4.  4.  4.  5.  5.  5.  5.  5.  5.  6.  6.  6.  6.\n",
      "  6.  6.  6.  6.  6.  6.  6.  6.  7.  7.  7.  7.  7.  7.  7.  7.  7.  7.\n",
      "  7.  7.  7.  7.  7.  7.  7.  7.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9. 11. 12. 14. 14. 14. 14. 14.\n",
      " 15. 17. 17. 18. 21. 22. 23. 24. 24. 25. 25. 26. 27. 27. 28. 28. 28. 28.\n",
      " 28. 29. 29. 29. 32. 33. 33. 35. 35. 35. 36. 36. 36. 37. 37. 38. 39. 39.\n",
      " 39. 39. 39.]\n",
      "Outliers for Measles\n",
      "[   944    956    982    998    999   1005   1006   1010   1014   1028\n",
      "   1043   1064   1088   1089   1116   1129   1141   1142   1147   1154\n",
      "   1156   1159   1166   1176   1190   1204   1205   1217   1218   1222\n",
      "   1224   1238   1272   1275   1279   1282   1292   1296   1313   1317\n",
      "   1319   1329   1333   1356   1370   1385   1395   1407   1415   1446\n",
      "   1449   1455   1457   1459   1461   1469   1483   1516   1536   1541\n",
      "   1569   1588   1599   1601   1607   1613   1617   1686   1719   1761\n",
      "   1771   1783   1847   1861   1865   1868   1939   1978   1986   1989\n",
      "   1990   2027   2046   2072   2089   2107   2123   2144   2175   2198\n",
      "   2200   2204   2248   2249   2281   2299   2302   2307   2321   2339\n",
      "   2359   2392   2395   2444   2486   2488   2511   2583   2613   2632\n",
      "   2641   2660   2672   2724   2738   2787   2801   2807   2838   2861\n",
      "   2920   2924   2939   2981   3000   3013   3025   3138   3156   3188\n",
      "   3196   3255   3289   3291   3312   3344   3362   3511   3526   3588\n",
      "   3776   3802   3825   3863   3893   4023   4067   4074   4165   4189\n",
      "   4211   4235   4248   4321   4347   4353   4386   4448   4458   4519\n",
      "   4657   4711   4740   4779   5023   5043   5048   5185   5189   5197\n",
      "   5253   5494   5625   5729   5736   5844   5857   6000   6024   6071\n",
      "   6183   6187   6192   6200   6368   6399   6447   6532   6538   6847\n",
      "   6855   7003   7085   7120   7135   7155   7319   7360   7362   7368\n",
      "   7405   7450   7587   7641   7790   7823   7872   7878   8046   8419\n",
      "   8491   8523   8650   8749   8762   8927   9081   9385   9396   9696\n",
      "   9743   9943   9960  10000  10315  10511  10677  10795  10841  10982\n",
      "  11699  12074  12289  12499  12598  12739  12746  12943  13234  13344\n",
      "  13822  14492  14949  15369  15489  15754  15853  16118  16244  16970\n",
      "  17620  18668  18843  18869  19456  20422  20818  21893  22004  24457\n",
      "  25934  26883  28898  29171  29429  30328  30509  31458  33634  35558\n",
      "  36711  38159  41144  42724  44258  49871  52461  52628  52852  54118\n",
      "  55443  56188  58341  58848  62233  64185  70549  71093  71879  79563\n",
      "  88962  99602 109023 110927 118712 124219 131441]\n",
      "Outliers for BMI\n",
      "[]\n",
      "Outliers for Under Five Death\n",
      "[  72   73   73   73   74   74   75   75   76   76   76   77   78   78\n",
      "   78   78   79   79   79   79   79   79   79   80   80   80   80   80\n",
      "   81   82   82   82   82   83   83   83   83   84   84   84   84   85\n",
      "   85   85   86   86   86   86   86   87   87   87   87   88   88   89\n",
      "   89   89   89   89   89   90   90   90   90   90   91   91   91   93\n",
      "   93   93   93   93   93   94   94   95   95   96   96   97   97   98\n",
      "   98   99  100  100  101  101  101  102  103  103  104  104  105  106\n",
      "  106  108  108  109  109  110  110  112  113  113  114  115  115  116\n",
      "  117  118  118  118  119  120  120  121  121  122  122  122  122  124\n",
      "  124  127  127  127  130  130  132  133  137  138  139  142  145  148\n",
      "  150  154  155  161  161  164  167  173  173  174  181  181  186  188\n",
      "  194  198  201  201  202  211  215  215  216  221  222  229  231  231\n",
      "  233  237  237  243  247  251  256  264  268  270  286  288  308  332\n",
      "  360  392  427  442  450  456  458  459  459  459  460  460  461  461\n",
      "  466  467  511  558  608  759  773  788  802  817  832  848  863  879\n",
      "  893 1200 1300 1400 1500 1600 1700 1800 1900 2000 2000 2100]\n",
      "Outliers for Polio\n",
      "[ 3.  4.  4.  5.  6.  6.  6.  6.  7.  7.  7.  7.  7.  7.  7.  7.  7.  7.\n",
      "  7.  7.  7.  7.  7.  7.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9. 23. 24. 24. 26. 32. 35. 36. 37. 41. 42.\n",
      " 42. 43. 44. 44. 44. 45. 45. 45. 45. 46. 46. 46. 46. 46. 47. 47. 47. 48.\n",
      " 49. 49. 49. 51. 51. 52. 52. 53. 54. 54. 54. 54. 54. 55. 55. 56.]\n",
      "Outliers for Total Expenditure\n",
      "[12.23 12.24 12.6  13.13 13.66 13.73 14.39]\n",
      "Outliers for Diphtheria\n",
      "[ 2.  4.  4.  5.  5.  5.  5.  5.  6.  6.  7.  7.  7.  7.  7.  7.  7.  7.\n",
      "  7.  7.  7.  7.  7.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9. 19. 23. 23.\n",
      " 24. 24. 26. 33. 33. 36. 36. 37. 39. 39. 39. 41. 42. 42. 42. 45. 45. 45.\n",
      " 46. 47. 47. 47. 48. 48. 49. 49. 49. 51. 52. 53. 53. 54. 54. 55. 55. 57.\n",
      " 57. 57. 58. 58. 58. 59. 59. 59.]\n",
      "Outliers for Hiv_Aids\n",
      "[ 1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.7  1.7\n",
      "  1.7  1.7  1.7  1.7  1.7  1.8  1.8  1.8  1.8  1.8  1.8  1.9  1.9  1.9\n",
      "  1.9  1.9  1.9  1.9  1.9  1.9  1.9  1.9  2.   2.   2.   2.   2.   2.\n",
      "  2.   2.   2.   2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.2\n",
      "  2.2  2.2  2.2  2.2  2.3  2.3  2.3  2.3  2.3  2.3  2.3  2.3  2.3  2.4\n",
      "  2.4  2.4  2.4  2.4  2.5  2.5  2.5  2.5  2.5  2.5  2.6  2.6  2.6  2.6\n",
      "  2.6  2.7  2.8  2.8  2.8  2.8  2.9  2.9  2.9  2.9  3.   3.   3.1  3.1\n",
      "  3.1  3.2  3.2  3.2  3.3  3.4  3.4  3.4  3.4  3.5  3.5  3.6  3.7  3.7\n",
      "  3.7  3.7  3.7  3.8  3.8  3.9  3.9  3.9  4.   4.   4.1  4.1  4.3  4.3\n",
      "  4.3  4.3  4.4  4.4  4.4  4.4  4.5  4.5  4.5  4.6  4.6  4.7  4.7  4.7\n",
      "  4.8  4.8  4.8  4.8  4.9  4.9  4.9  5.   5.   5.   5.1  5.1  5.1  5.1\n",
      "  5.2  5.3  5.3  5.4  5.5  5.5  5.6  5.7  5.8  5.9  6.   6.   6.2  6.2\n",
      "  6.2  6.3  6.3  6.3  6.3  6.4  6.6  6.7  6.8  6.8  6.8  6.9  7.   7.\n",
      "  7.1  7.2  7.3  7.3  7.4  7.6  7.7  8.   8.   8.   8.1  8.1  8.2  8.3\n",
      "  8.4  8.5  8.6  8.7  8.8  8.9  8.9  9.   9.   9.1  9.1  9.4  9.4  9.6\n",
      "  9.6  9.8 10.  10.  10.3 10.5 10.7 10.8 10.9 11.  11.2 11.3 11.6 11.9\n",
      " 12.2 12.6 12.7 13.2 13.3 13.4 13.4 13.4 13.6 13.7 14.1 14.4 14.5 14.5\n",
      " 14.9 15.3 15.7 15.7 15.7 15.9 15.9 16.2 16.3 16.9 17.  18.1 18.2 19.\n",
      " 19.3 20.5 20.6 21.1 21.3 21.6 22.4 23.4 23.5 23.7 24.  24.2 24.7 26.4\n",
      " 26.6 26.8 27.3 28.1 28.4 28.9 29.5 29.7 30.  30.3 31.9 33.6 33.7 33.8\n",
      " 34.1 34.6 34.6 34.8 36.7 37.2 38.8 39.8 40.2 40.7 42.1 43.5 43.7 46.4\n",
      " 48.8 49.1 49.9 50.3 50.6]\n",
      "Outliers for GDP\n",
      "[ 11122.8627   11183.96191  11224.1548   11326.21947  11341.13395\n",
      "  11527.59323  11634.42422  11635.2637   11685.97983  11729.14616\n",
      "  11837.3913   11938.212    11984.86857  12127.22522  12154.82994\n",
      "  12189.9516   12216.9446   12219.37379  12245.25645  12291.46685\n",
      "  12297.91535  12323.1275   12387.18982  12538.17883  12542.93565\n",
      "  12593.7374   12595.4165   12599.52486  12726.9836   12845.17363\n",
      "  12882.28897  12969.7712   12976.63642  13145.1484   13167.47289\n",
      "  13198.6693   13235.97757  13467.46827  13546.69872  13574.749\n",
      "  13689.98999  13822.8559   13893.18768  14125.961    14157.14416\n",
      "  14166.4998   14212.687    14341.675    14341.836    14357.73679\n",
      "  14539.18421  14638.6482   14672.8826   14676.769    14726.31828\n",
      "  14817.37778  14961.56818  14965.361    15154.45687  15323.6114\n",
      "  15431.9293   15543.67653  15687.34795  15712.82376  15725.1374\n",
      "  15772.7334   15835.34667  15893.8656   15941.39722  16348.53121\n",
      "  16372.29152  16554.97139  16586.452    16671.5672   16683.94578\n",
      "  16737.89827  16749.136    16881.2568   17421.8922   17454.84342\n",
      "  18431.15696  18477.57841  18784.9485   18946.99942  19152.531\n",
      "  19325.24283  19375.5696   19517.84168  19821.44463  19888.172\n",
      "  19941.45532  21188.11857  21495.7741   21618.73534  21673.7817\n",
      "  21874.8195   21928.767    21955.149    22196.5736   22242.68193\n",
      "  22465.64184  22527.31775  22538.6548   22551.73574  22821.847\n",
      "  23121.56635  23196.18375  23465.38559  23687.31689  23718.7467\n",
      "  23932.49128  24275.2426   24489.73553  24517.26745  24815.6884\n",
      "  24918.64584  25324.48666  25916.29353  26351.37568  26917.75898\n",
      "  27169.99256  27387.2258   27795.87671  28172.14883  28482.6948\n",
      "  28562.29324  28827.32636  28951.15556  29657.4421   29691.18158\n",
      "  31174.56118  31386.63265  31834.21816  31959.26215  31979.87195\n",
      "  31997.2821   32233.83942  32333.4661   32569.59683  33657.15727\n",
      "  33874.74255  34165.9343   34696.6292   34814.12436  34879.72633\n",
      "  35396.66517  35578.73619  35589.71295  35849.37364  36118.28381\n",
      "  36189.58838  36393.66932  36447.87232  36544.5853   36693.4262\n",
      "  36967.28292  36976.84512  37582.84624  37698.78573  38242.4252\n",
      "  38334.68477  38852.3613   41631.13141  41732.7725   41785.55691\n",
      "  41814.8191   42554.1225   42742.99898  42955.24287  44544.5268\n",
      "  45413.6571   45699.19832  46586.6525   46596.33599  46657.629\n",
      "  47439.39684  47447.4762   47654.18721  48179.4285   48333.57273\n",
      "  48424.58927  48538.58776  48735.99549  49231.36254  49474.7561\n",
      "  49664.6854   51126.74139  51322.63997  51386.37665  51574.48942\n",
      "  51874.848    52157.4687   52413.72116  52496.69487  52567.52568\n",
      "  57134.777    59593.28711  62214.6912   62245.129    65445.8853\n",
      "  67677.63477  67792.3386   75716.3518   89739.7117  113751.85\n",
      " 114293.8433  115761.577   119172.7418 ]\n",
      "Outliers for Population\n",
      "[1.89124120e+07 1.89149770e+07 1.91266370e+07 1.91396580e+07\n",
      " 1.91482190e+07 1.92979290e+07 1.94325410e+07 1.94335230e+07\n",
      " 1.97165980e+07 1.99245220e+07 1.99836930e+07 1.99964690e+07\n",
      " 2.11544290e+07 2.13196850e+07 2.14517480e+07 2.15474630e+07\n",
      " 2.15743260e+07 2.16557150e+07 2.17439490e+07 2.19799230e+07\n",
      " 2.21134250e+07 2.21883870e+07 2.23465730e+07 2.24429710e+07\n",
      " 2.25452140e+07 2.25495470e+07 2.26712730e+07 2.27282540e+07\n",
      " 2.28467580e+07 2.29611460e+07 2.29838220e+07 2.31173530e+07\n",
      " 2.33691310e+07 2.39344780e+07 2.41189790e+07 2.41988110e+07\n",
      " 2.42131330e+07 2.42185650e+07 2.45775110e+07 2.51217960e+07\n",
      " 2.52322630e+07 2.56593930e+07 2.61435660e+07 2.62148470e+07\n",
      " 2.63462510e+07 2.64343720e+07 2.64758590e+07 2.66167920e+07\n",
      " 2.66258450e+07 2.69377380e+07 2.69625630e+07 2.72123820e+07\n",
      " 2.72731940e+07 2.73271470e+07 2.75684360e+07 2.76499250e+07\n",
      " 2.76979120e+07 2.79499440e+07 2.81122890e+07 2.82927240e+07\n",
      " 2.83232410e+07 2.86351280e+07 2.88496210e+07 2.91114170e+07\n",
      " 2.91818320e+07 2.93736460e+07 2.95123680e+07 2.97599890e+07\n",
      " 2.98439370e+07 2.98946520e+07 3.12258810e+07 3.15968550e+07\n",
      " 3.16638960e+07 3.17316880e+07 3.19898970e+07 3.27718950e+07\n",
      " 3.27765710e+07 3.28588230e+07 3.28879280e+07 3.32457730e+07\n",
      " 3.32884370e+07 3.33337890e+07 3.36285710e+07 3.37364940e+07\n",
      " 3.37779150e+07 3.38247690e+07 3.38831450e+07 3.39151330e+07\n",
      " 3.51554510e+07 3.55445640e+07 3.61176370e+07 3.68195580e+07\n",
      " 3.75537260e+07 3.75658470e+07 3.81257590e+07 3.81412670e+07\n",
      " 3.81654450e+07 3.81822220e+07 3.82586290e+07 3.83385620e+07\n",
      " 3.87286960e+07 3.88333380e+07 3.91133130e+07 3.91454880e+07\n",
      " 3.91484160e+07 4.12238890e+07 4.14315580e+07 4.15724910e+07\n",
      " 4.16568790e+07 4.21521510e+07 4.21876450e+07 4.24868390e+07\n",
      " 4.25399250e+07 4.27241630e+07 4.29218950e+07 4.29815150e+07\n",
      " 4.32856340e+07 4.36466290e+07 4.36531550e+07 4.38357220e+07\n",
      " 4.43745720e+07 4.43973190e+07 4.48268490e+07 4.48968560e+07\n",
      " 4.52719470e+07 4.53129370e+07 4.54161810e+07 4.58554829e+07\n",
      " 4.63629460e+07 4.64181939e+07 4.65768970e+07 4.67426970e+07\n",
      " 4.68814750e+07 4.73429810e+07 4.76248940e+07 4.77919110e+07\n",
      " 4.84826140e+07 4.86838650e+07 4.88464740e+07 4.88838450e+07\n",
      " 4.91715860e+07 4.91758480e+07 4.94797520e+07 4.95575733e+07\n",
      " 5.14481960e+07 5.17293454e+07 5.19241820e+07 5.33119556e+07\n",
      " 5.41467347e+07 5.76853270e+07 5.79694840e+07 5.81439790e+07\n",
      " 5.88267310e+07 5.92774170e+07 5.93794490e+07 5.95397170e+07\n",
      " 6.13574310e+07 6.22448840e+07 6.31793560e+07 6.35433220e+07\n",
      " 6.36213760e+07 6.41914740e+07 6.45549520e+07 6.53427760e+07\n",
      " 6.58241640e+07 6.61956150e+07 6.63319570e+07 6.68818670e+07\n",
      " 6.78439790e+07 6.84167720e+07 6.95972810e+07 7.13391850e+07\n",
      " 7.23269140e+07 7.45698670e+07 7.57873330e+07 7.79915690e+07\n",
      " 7.96653150e+07 8.22663720e+07 8.23499250e+07 8.23764510e+07\n",
      " 8.24694220e+07 8.24884950e+07 8.25341760e+07 8.31848920e+07\n",
      " 8.46784930e+07 8.54162530e+07 8.62742370e+07 9.24441830e+07\n",
      " 9.37266240e+07 9.48877240e+07 9.68666420e+07 9.73667740e+07\n",
      " 1.11836346e+08 1.17318941e+08 1.19714696e+08 1.21427132e+08\n",
      " 1.22535969e+08 1.24723629e+08 1.26365852e+08 1.27856227e+08\n",
      " 1.38939478e+08 1.42785342e+08 1.42849449e+08 1.43518523e+08\n",
      " 1.43819666e+08 1.44648257e+08 1.47139191e+08 1.53911916e+08\n",
      " 1.57571292e+08 1.58578261e+08 1.67297284e+08 1.74184265e+08\n",
      " 1.75287587e+08 1.77911533e+08 1.81712595e+08 1.82482149e+08\n",
      " 1.84738458e+08 1.85546257e+08 1.86917361e+08 1.94895996e+08\n",
      " 1.96796269e+08 1.98686688e+08 2.23614649e+08 2.32989141e+08\n",
      " 2.36159276e+08 2.42524123e+08 2.48883232e+08 2.55131116e+08\n",
      " 1.12613578e+09 1.14411867e+09 1.16197772e+09 1.17968124e+09\n",
      " 1.29385929e+09]\n",
      "Outliers for Thin_1_19_Years\n",
      "[15.4 15.4 15.4 15.4 15.4 15.4 15.4 15.7 15.9 15.9 16.1 16.1 16.3 16.3\n",
      " 16.3 16.5 16.6 16.7 16.8 16.9 17.  17.1 17.2 17.2 17.3 17.4 17.5 17.5\n",
      " 17.6 17.7 17.8 17.8 17.9 18.  18.  18.1 18.2 18.3 18.3 18.4 18.5 18.6\n",
      " 18.6 18.7 18.8 18.9 18.9 19.  19.1 19.2 19.2 19.3 19.3 19.4 19.5 19.5\n",
      " 19.6 19.7 19.7 19.8 19.9 19.9 21.  21.2 21.4 21.6 26.8 26.8 26.9 26.9\n",
      " 27.  27.  27.  27.1 27.1 27.2 27.2]\n",
      "Outliers for Thin_5_9_Year\n",
      "[15.3 15.3 15.4 15.4 15.5 15.5 15.5 15.5 15.5 16.1 16.2 16.3 16.5 16.5\n",
      " 16.7 16.7 16.9 17.  17.1 17.2 17.3 17.3 17.4 17.5 17.5 17.6 17.7 17.8\n",
      " 17.8 18.  18.  18.1 18.1 18.2 18.2 18.3 18.4 18.4 18.6 18.6 18.6 18.7\n",
      " 18.8 18.8 18.9 19.  19.1 19.1 19.1 19.2 19.3 19.4 19.4 19.5 19.6 19.7\n",
      " 19.7 19.8 19.9 19.9 19.9 21.1 21.3 21.5 21.7 21.8 22.  27.4 27.5 27.6\n",
      " 27.7 27.8 27.8 27.9 28.  28.  28.1 28.2]\n",
      "Outliers for Income Comp Resource\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Outliers for Schooling\n",
      "[ 4.2  4.4  4.5  4.5  4.7  4.7 19.8 20.1 20.1 20.3 20.3 20.3 20.4 20.5\n",
      " 20.6 20.7]\n"
     ]
    }
   ],
   "source": [
    "print(\"Outliers for Infant death\")\n",
    "Infant_death_indices,Infant_death_values = find_outliers_tukey(df1['infant deaths'])\n",
    "print(np.sort(Infant_death_values))\n",
    "print(\"Outliers for Alcohol\")\n",
    "Alcohol_indices, Alcohol_values = find_outliers_tukey(df1['Alcohol'])\n",
    "print(np.sort(Alcohol_values))\n",
    "print(\"Outliers for Percentage Expenditure\")\n",
    "Percentage_Expenditure_indices, Percentage_Expenditure_values = find_outliers_tukey(df1['percentage expenditure'])\n",
    "print(np.sort(Percentage_Expenditure_values))\n",
    "print(\"Outliers for Hepatitis B\")\n",
    "Hepatitis_B_indices, Hepatitis_B_values = find_outliers_tukey(df1['Hepatitis B'])\n",
    "print(np.sort(Hepatitis_B_values))\n",
    "print(\"Outliers for Measles\")\n",
    "Measles_indices, Measles_values = find_outliers_tukey(df1[\"Measles \"])\n",
    "print(np.sort(Measles_values))\n",
    "print(\"Outliers for BMI\")\n",
    "BMI_indices, BMI_values = find_outliers_tukey(df1[' BMI '])\n",
    "print(np.sort(BMI_values))\n",
    "print(\"Outliers for Under Five Death\")\n",
    "Under_Five_Death_indices, Under_Five_Death_values = find_outliers_tukey(df1['under-five deaths '])\n",
    "print(np.sort(Under_Five_Death_values))\n",
    "print(\"Outliers for Polio\")\n",
    "Polio_indices, Polio_values = find_outliers_tukey(df1['Polio'])\n",
    "print(np.sort(Polio_values))\n",
    "print(\"Outliers for Total Expenditure\")\n",
    "Total_Expenditure_indices, Total_Expenditure_values = find_outliers_tukey(df1['Total expenditure'])\n",
    "print(np.sort(Total_Expenditure_values))\n",
    "print(\"Outliers for Diphtheria\")\n",
    "Diphtheria_indices, Diphtheria_values = find_outliers_tukey(df1['Diphtheria '])\n",
    "print(np.sort(Diphtheria_values))\n",
    "print(\"Outliers for Hiv_Aids\")\n",
    "Hiv_Aids_indices, Hiv_Aids_values = find_outliers_tukey(df1[' HIV/AIDS'])\n",
    "print(np.sort(Hiv_Aids_values))\n",
    "print(\"Outliers for GDP\")\n",
    "GDP_indices, GDP_values = find_outliers_tukey(df1['GDP'])\n",
    "print(np.sort(GDP_values))\n",
    "print(\"Outliers for Population\")\n",
    "Population_indices, Population_values = find_outliers_tukey(df1['Population'])\n",
    "print(np.sort(Population_values))\n",
    "print(\"Outliers for Thin_1_19_Years\")\n",
    "Thin_1_19_Years_indices, Thin_1_19_Years_values = find_outliers_tukey(df1[' thinness  1-19 years'])\n",
    "print(np.sort(Thin_1_19_Years_values))\n",
    "print(\"Outliers for Thin_5_9_Year\")\n",
    "Thin_5_9_Year_indices, Thin_5_9_Year_values = find_outliers_tukey(df1[' thinness 5-9 years'])\n",
    "print(np.sort(Thin_5_9_Year_values))\n",
    "print(\"Outliers for Income Comp Resource\")\n",
    "Income_Comp_Resource_indices, Income_Comp_Resource_values = find_outliers_tukey(df1['Income composition of resources'])\n",
    "print(np.sort(Income_Comp_Resource_values))\n",
    "print(\"Outliers for Schooling\")\n",
    "Schooling_indices, Schooling_values = find_outliers_tukey(df1['Schooling'])\n",
    "print(np.sort(Schooling_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1.649000e+03</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.840509</td>\n",
       "      <td>69.302304</td>\n",
       "      <td>168.215282</td>\n",
       "      <td>1.928441</td>\n",
       "      <td>4.533196</td>\n",
       "      <td>51.878248</td>\n",
       "      <td>79.217708</td>\n",
       "      <td>10.628866</td>\n",
       "      <td>38.128623</td>\n",
       "      <td>2.529412</td>\n",
       "      <td>83.564585</td>\n",
       "      <td>5.955925</td>\n",
       "      <td>84.155246</td>\n",
       "      <td>1.983869</td>\n",
       "      <td>1187.666606</td>\n",
       "      <td>1.465363e+07</td>\n",
       "      <td>4.850637</td>\n",
       "      <td>4.907762</td>\n",
       "      <td>0.631551</td>\n",
       "      <td>12.119891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.087711</td>\n",
       "      <td>8.796834</td>\n",
       "      <td>125.310417</td>\n",
       "      <td>1.276077</td>\n",
       "      <td>4.029189</td>\n",
       "      <td>23.510089</td>\n",
       "      <td>25.604664</td>\n",
       "      <td>10.574656</td>\n",
       "      <td>19.754249</td>\n",
       "      <td>1.689636</td>\n",
       "      <td>22.450557</td>\n",
       "      <td>2.299385</td>\n",
       "      <td>21.579193</td>\n",
       "      <td>6.032360</td>\n",
       "      <td>696.665625</td>\n",
       "      <td>7.046039e+07</td>\n",
       "      <td>4.599228</td>\n",
       "      <td>4.653757</td>\n",
       "      <td>0.183089</td>\n",
       "      <td>2.795388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>64.400000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.810000</td>\n",
       "      <td>37.438577</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>4.410000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>462.149650</td>\n",
       "      <td>1.918970e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.509000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>71.700000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.790000</td>\n",
       "      <td>64.884537</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.700000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.840000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1592.572182</td>\n",
       "      <td>1.419631e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.673000</td>\n",
       "      <td>12.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.340000</td>\n",
       "      <td>64.884537</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>55.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.470000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>7.658972e+06</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.751000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>99.080954</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>77.100000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>14.390000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>2494.353552</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>0.936000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "count  1649.000000       1649.000000      1649.000000    1649.000000   \n",
       "mean   2007.840509         69.302304       168.215282       1.928441   \n",
       "std       4.087711          8.796834       125.310417       1.276077   \n",
       "min    2000.000000         44.000000         1.000000       0.000000   \n",
       "25%    2005.000000         64.400000        77.000000       1.000000   \n",
       "50%    2008.000000         71.700000       148.000000       3.000000   \n",
       "75%    2011.000000         75.000000       227.000000       3.000000   \n",
       "max    2015.000000         89.000000       723.000000       3.000000   \n",
       "\n",
       "           Alcohol  percentage expenditure  Hepatitis B     Measles   \\\n",
       "count  1649.000000             1649.000000  1649.000000  1649.000000   \n",
       "mean      4.533196               51.878248    79.217708    10.628866   \n",
       "std       4.029189               23.510089    25.604664    10.574656   \n",
       "min       0.010000                0.000000     2.000000     0.000000   \n",
       "25%       0.810000               37.438577    74.000000     0.000000   \n",
       "50%       3.790000               64.884537    89.000000    15.000000   \n",
       "75%       7.340000               64.884537    96.000000    17.000000   \n",
       "max      17.870000               99.080954    99.000000    50.000000   \n",
       "\n",
       "              BMI   under-five deaths         Polio  Total expenditure  \\\n",
       "count  1649.000000         1649.000000  1649.000000        1649.000000   \n",
       "mean     38.128623            2.529412    83.564585           5.955925   \n",
       "std      19.754249            1.689636    22.450557           2.299385   \n",
       "min       2.000000            0.000000     3.000000           0.740000   \n",
       "25%      19.500000            1.000000    81.000000           4.410000   \n",
       "50%      43.700000            4.000000    93.000000           5.840000   \n",
       "75%      55.800000            4.000000    97.000000           7.470000   \n",
       "max      77.100000            4.000000    99.000000          14.390000   \n",
       "\n",
       "       Diphtheria      HIV/AIDS          GDP    Population  \\\n",
       "count  1649.000000  1649.000000  1649.000000  1.649000e+03   \n",
       "mean     84.155246     1.983869  1187.666606  1.465363e+07   \n",
       "std      21.579193     6.032360   696.665625  7.046039e+07   \n",
       "min       2.000000     0.100000     1.681350  3.400000e+01   \n",
       "25%      82.000000     0.100000   462.149650  1.918970e+05   \n",
       "50%      92.000000     0.100000  1592.572182  1.419631e+06   \n",
       "75%      97.000000     0.700000  1766.947595  7.658972e+06   \n",
       "max      99.000000    50.600000  2494.353552  1.293859e+09   \n",
       "\n",
       "        thinness  1-19 years   thinness 5-9 years  \\\n",
       "count            1649.000000          1649.000000   \n",
       "mean                4.850637             4.907762   \n",
       "std                 4.599228             4.653757   \n",
       "min                 0.100000             0.100000   \n",
       "25%                 1.600000             1.700000   \n",
       "50%                 3.000000             3.200000   \n",
       "75%                 7.100000             7.100000   \n",
       "max                27.200000            28.200000   \n",
       "\n",
       "       Income composition of resources    Schooling  \n",
       "count                      1649.000000  1649.000000  \n",
       "mean                          0.631551    12.119891  \n",
       "std                           0.183089     2.795388  \n",
       "min                           0.000000     4.200000  \n",
       "25%                           0.509000    10.300000  \n",
       "50%                           0.673000    12.300000  \n",
       "75%                           0.751000    14.000000  \n",
       "max                           0.936000    20.700000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1.columns\n",
    "#df_del = df1.drop(bp_indices)\n",
    "df1[\"infant deaths\"] = np.where(df1[\"infant deaths\"] >3, 3,df1['infant deaths'])\n",
    "df1[\"percentage expenditure\"] = np.where(df1[\"percentage expenditure\"] >100,64.8845366125,df1['percentage expenditure'])\n",
    "df1[\"Measles \"] = np.where(df1[\"Measles \"] >50,17,df1['Measles '])\n",
    "df1[\"under-five deaths \"] = np.where(df1[\"under-five deaths \"] >4,4,df1['under-five deaths '])\n",
    "df1[\"GDP\"] = np.where(df1[\"GDP\"] >2500,1766.947595,df1['GDP'])\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers for Infant death\n",
      "[]\n",
      "Outliers for Alcohol\n",
      "[17.31 17.87]\n",
      "Outliers for Percentage Expenditure\n",
      "[]\n",
      "Outliers for Hepatitis B\n",
      "[ 2.  2.  2.  2.  4.  4.  4.  4.  5.  5.  5.  5.  5.  5.  6.  6.  6.  6.\n",
      "  6.  6.  6.  6.  6.  6.  6.  6.  7.  7.  7.  7.  7.  7.  7.  7.  7.  7.\n",
      "  7.  7.  7.  7.  7.  7.  7.  7.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9. 11. 12. 14. 14. 14. 14. 14.\n",
      " 15. 17. 17. 18. 21. 22. 23. 24. 24. 25. 25. 26. 27. 27. 28. 28. 28. 28.\n",
      " 28. 29. 29. 29. 32. 33. 33. 35. 35. 35. 36. 36. 36. 37. 37. 38. 39. 39.\n",
      " 39. 39. 39.]\n",
      "Outliers for Measles\n",
      "[43 43 43 44 44 44 44 44 45 45 45 45 45 46 46 47 47 47 48 48 48 48 48 48\n",
      " 49 49 49 49 50 50 50 50]\n",
      "Outliers for BMI\n",
      "[]\n",
      "Outliers for Under Five Death\n",
      "[]\n",
      "Outliers for Polio\n",
      "[ 3.  4.  4.  5.  6.  6.  6.  6.  7.  7.  7.  7.  7.  7.  7.  7.  7.  7.\n",
      "  7.  7.  7.  7.  7.  7.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9. 23. 24. 24. 26. 32. 35. 36. 37. 41. 42.\n",
      " 42. 43. 44. 44. 44. 45. 45. 45. 45. 46. 46. 46. 46. 46. 47. 47. 47. 48.\n",
      " 49. 49. 49. 51. 51. 52. 52. 53. 54. 54. 54. 54. 54. 55. 55. 56.]\n",
      "Outliers for Total Expenditure\n",
      "[12.23 12.24 12.6  13.13 13.66 13.73 14.39]\n",
      "Outliers for Diphtheria\n",
      "[ 2.  4.  4.  5.  5.  5.  5.  5.  6.  6.  7.  7.  7.  7.  7.  7.  7.  7.\n",
      "  7.  7.  7.  7.  7.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.\n",
      "  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  8.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.\n",
      "  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9.  9. 19. 23. 23.\n",
      " 24. 24. 26. 33. 33. 36. 36. 37. 39. 39. 39. 41. 42. 42. 42. 45. 45. 45.\n",
      " 46. 47. 47. 47. 48. 48. 49. 49. 49. 51. 52. 53. 53. 54. 54. 55. 55. 57.\n",
      " 57. 57. 58. 58. 58. 59. 59. 59.]\n",
      "Outliers for Hiv_Aids\n",
      "[ 1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.6  1.7  1.7\n",
      "  1.7  1.7  1.7  1.7  1.7  1.8  1.8  1.8  1.8  1.8  1.8  1.9  1.9  1.9\n",
      "  1.9  1.9  1.9  1.9  1.9  1.9  1.9  1.9  2.   2.   2.   2.   2.   2.\n",
      "  2.   2.   2.   2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.1  2.2\n",
      "  2.2  2.2  2.2  2.2  2.3  2.3  2.3  2.3  2.3  2.3  2.3  2.3  2.3  2.4\n",
      "  2.4  2.4  2.4  2.4  2.5  2.5  2.5  2.5  2.5  2.5  2.6  2.6  2.6  2.6\n",
      "  2.6  2.7  2.8  2.8  2.8  2.8  2.9  2.9  2.9  2.9  3.   3.   3.1  3.1\n",
      "  3.1  3.2  3.2  3.2  3.3  3.4  3.4  3.4  3.4  3.5  3.5  3.6  3.7  3.7\n",
      "  3.7  3.7  3.7  3.8  3.8  3.9  3.9  3.9  4.   4.   4.1  4.1  4.3  4.3\n",
      "  4.3  4.3  4.4  4.4  4.4  4.4  4.5  4.5  4.5  4.6  4.6  4.7  4.7  4.7\n",
      "  4.8  4.8  4.8  4.8  4.9  4.9  4.9  5.   5.   5.   5.1  5.1  5.1  5.1\n",
      "  5.2  5.3  5.3  5.4  5.5  5.5  5.6  5.7  5.8  5.9  6.   6.   6.2  6.2\n",
      "  6.2  6.3  6.3  6.3  6.3  6.4  6.6  6.7  6.8  6.8  6.8  6.9  7.   7.\n",
      "  7.1  7.2  7.3  7.3  7.4  7.6  7.7  8.   8.   8.   8.1  8.1  8.2  8.3\n",
      "  8.4  8.5  8.6  8.7  8.8  8.9  8.9  9.   9.   9.1  9.1  9.4  9.4  9.6\n",
      "  9.6  9.8 10.  10.  10.3 10.5 10.7 10.8 10.9 11.  11.2 11.3 11.6 11.9\n",
      " 12.2 12.6 12.7 13.2 13.3 13.4 13.4 13.4 13.6 13.7 14.1 14.4 14.5 14.5\n",
      " 14.9 15.3 15.7 15.7 15.7 15.9 15.9 16.2 16.3 16.9 17.  18.1 18.2 19.\n",
      " 19.3 20.5 20.6 21.1 21.3 21.6 22.4 23.4 23.5 23.7 24.  24.2 24.7 26.4\n",
      " 26.6 26.8 27.3 28.1 28.4 28.9 29.5 29.7 30.  30.3 31.9 33.6 33.7 33.8\n",
      " 34.1 34.6 34.6 34.8 36.7 37.2 38.8 39.8 40.2 40.7 42.1 43.5 43.7 46.4\n",
      " 48.8 49.1 49.9 50.3 50.6]\n",
      "Outliers for GDP\n",
      "[]\n",
      "Outliers for Population\n",
      "[1.89124120e+07 1.89149770e+07 1.91266370e+07 1.91396580e+07\n",
      " 1.91482190e+07 1.92979290e+07 1.94325410e+07 1.94335230e+07\n",
      " 1.97165980e+07 1.99245220e+07 1.99836930e+07 1.99964690e+07\n",
      " 2.11544290e+07 2.13196850e+07 2.14517480e+07 2.15474630e+07\n",
      " 2.15743260e+07 2.16557150e+07 2.17439490e+07 2.19799230e+07\n",
      " 2.21134250e+07 2.21883870e+07 2.23465730e+07 2.24429710e+07\n",
      " 2.25452140e+07 2.25495470e+07 2.26712730e+07 2.27282540e+07\n",
      " 2.28467580e+07 2.29611460e+07 2.29838220e+07 2.31173530e+07\n",
      " 2.33691310e+07 2.39344780e+07 2.41189790e+07 2.41988110e+07\n",
      " 2.42131330e+07 2.42185650e+07 2.45775110e+07 2.51217960e+07\n",
      " 2.52322630e+07 2.56593930e+07 2.61435660e+07 2.62148470e+07\n",
      " 2.63462510e+07 2.64343720e+07 2.64758590e+07 2.66167920e+07\n",
      " 2.66258450e+07 2.69377380e+07 2.69625630e+07 2.72123820e+07\n",
      " 2.72731940e+07 2.73271470e+07 2.75684360e+07 2.76499250e+07\n",
      " 2.76979120e+07 2.79499440e+07 2.81122890e+07 2.82927240e+07\n",
      " 2.83232410e+07 2.86351280e+07 2.88496210e+07 2.91114170e+07\n",
      " 2.91818320e+07 2.93736460e+07 2.95123680e+07 2.97599890e+07\n",
      " 2.98439370e+07 2.98946520e+07 3.12258810e+07 3.15968550e+07\n",
      " 3.16638960e+07 3.17316880e+07 3.19898970e+07 3.27718950e+07\n",
      " 3.27765710e+07 3.28588230e+07 3.28879280e+07 3.32457730e+07\n",
      " 3.32884370e+07 3.33337890e+07 3.36285710e+07 3.37364940e+07\n",
      " 3.37779150e+07 3.38247690e+07 3.38831450e+07 3.39151330e+07\n",
      " 3.51554510e+07 3.55445640e+07 3.61176370e+07 3.68195580e+07\n",
      " 3.75537260e+07 3.75658470e+07 3.81257590e+07 3.81412670e+07\n",
      " 3.81654450e+07 3.81822220e+07 3.82586290e+07 3.83385620e+07\n",
      " 3.87286960e+07 3.88333380e+07 3.91133130e+07 3.91454880e+07\n",
      " 3.91484160e+07 4.12238890e+07 4.14315580e+07 4.15724910e+07\n",
      " 4.16568790e+07 4.21521510e+07 4.21876450e+07 4.24868390e+07\n",
      " 4.25399250e+07 4.27241630e+07 4.29218950e+07 4.29815150e+07\n",
      " 4.32856340e+07 4.36466290e+07 4.36531550e+07 4.38357220e+07\n",
      " 4.43745720e+07 4.43973190e+07 4.48268490e+07 4.48968560e+07\n",
      " 4.52719470e+07 4.53129370e+07 4.54161810e+07 4.58554829e+07\n",
      " 4.63629460e+07 4.64181939e+07 4.65768970e+07 4.67426970e+07\n",
      " 4.68814750e+07 4.73429810e+07 4.76248940e+07 4.77919110e+07\n",
      " 4.84826140e+07 4.86838650e+07 4.88464740e+07 4.88838450e+07\n",
      " 4.91715860e+07 4.91758480e+07 4.94797520e+07 4.95575733e+07\n",
      " 5.14481960e+07 5.17293454e+07 5.19241820e+07 5.33119556e+07\n",
      " 5.41467347e+07 5.76853270e+07 5.79694840e+07 5.81439790e+07\n",
      " 5.88267310e+07 5.92774170e+07 5.93794490e+07 5.95397170e+07\n",
      " 6.13574310e+07 6.22448840e+07 6.31793560e+07 6.35433220e+07\n",
      " 6.36213760e+07 6.41914740e+07 6.45549520e+07 6.53427760e+07\n",
      " 6.58241640e+07 6.61956150e+07 6.63319570e+07 6.68818670e+07\n",
      " 6.78439790e+07 6.84167720e+07 6.95972810e+07 7.13391850e+07\n",
      " 7.23269140e+07 7.45698670e+07 7.57873330e+07 7.79915690e+07\n",
      " 7.96653150e+07 8.22663720e+07 8.23499250e+07 8.23764510e+07\n",
      " 8.24694220e+07 8.24884950e+07 8.25341760e+07 8.31848920e+07\n",
      " 8.46784930e+07 8.54162530e+07 8.62742370e+07 9.24441830e+07\n",
      " 9.37266240e+07 9.48877240e+07 9.68666420e+07 9.73667740e+07\n",
      " 1.11836346e+08 1.17318941e+08 1.19714696e+08 1.21427132e+08\n",
      " 1.22535969e+08 1.24723629e+08 1.26365852e+08 1.27856227e+08\n",
      " 1.38939478e+08 1.42785342e+08 1.42849449e+08 1.43518523e+08\n",
      " 1.43819666e+08 1.44648257e+08 1.47139191e+08 1.53911916e+08\n",
      " 1.57571292e+08 1.58578261e+08 1.67297284e+08 1.74184265e+08\n",
      " 1.75287587e+08 1.77911533e+08 1.81712595e+08 1.82482149e+08\n",
      " 1.84738458e+08 1.85546257e+08 1.86917361e+08 1.94895996e+08\n",
      " 1.96796269e+08 1.98686688e+08 2.23614649e+08 2.32989141e+08\n",
      " 2.36159276e+08 2.42524123e+08 2.48883232e+08 2.55131116e+08\n",
      " 1.12613578e+09 1.14411867e+09 1.16197772e+09 1.17968124e+09\n",
      " 1.29385929e+09]\n",
      "Outliers for Thin_1_19_Years\n",
      "[15.4 15.4 15.4 15.4 15.4 15.4 15.4 15.7 15.9 15.9 16.1 16.1 16.3 16.3\n",
      " 16.3 16.5 16.6 16.7 16.8 16.9 17.  17.1 17.2 17.2 17.3 17.4 17.5 17.5\n",
      " 17.6 17.7 17.8 17.8 17.9 18.  18.  18.1 18.2 18.3 18.3 18.4 18.5 18.6\n",
      " 18.6 18.7 18.8 18.9 18.9 19.  19.1 19.2 19.2 19.3 19.3 19.4 19.5 19.5\n",
      " 19.6 19.7 19.7 19.8 19.9 19.9 21.  21.2 21.4 21.6 26.8 26.8 26.9 26.9\n",
      " 27.  27.  27.  27.1 27.1 27.2 27.2]\n",
      "Outliers for Thin_5_9_Year\n",
      "[15.3 15.3 15.4 15.4 15.5 15.5 15.5 15.5 15.5 16.1 16.2 16.3 16.5 16.5\n",
      " 16.7 16.7 16.9 17.  17.1 17.2 17.3 17.3 17.4 17.5 17.5 17.6 17.7 17.8\n",
      " 17.8 18.  18.  18.1 18.1 18.2 18.2 18.3 18.4 18.4 18.6 18.6 18.6 18.7\n",
      " 18.8 18.8 18.9 19.  19.1 19.1 19.1 19.2 19.3 19.4 19.4 19.5 19.6 19.7\n",
      " 19.7 19.8 19.9 19.9 19.9 21.1 21.3 21.5 21.7 21.8 22.  27.4 27.5 27.6\n",
      " 27.7 27.8 27.8 27.9 28.  28.  28.1 28.2]\n",
      "Outliers for Income Comp Resource\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Outliers for Schooling\n",
      "[ 4.2  4.4  4.5  4.5  4.7  4.7 19.8 20.1 20.1 20.3 20.3 20.3 20.4 20.5\n",
      " 20.6 20.7]\n"
     ]
    }
   ],
   "source": [
    "print(\"Outliers for Infant death\")\n",
    "Infant_death_indices,Infant_death_values = find_outliers_tukey(df1['infant deaths'])\n",
    "print(np.sort(Infant_death_values))\n",
    "print(\"Outliers for Alcohol\")\n",
    "Alcohol_indices, Alcohol_values = find_outliers_tukey(df1['Alcohol'])\n",
    "print(np.sort(Alcohol_values))\n",
    "print(\"Outliers for Percentage Expenditure\")\n",
    "Percentage_Expenditure_indices, Percentage_Expenditure_values = find_outliers_tukey(df1['percentage expenditure'])\n",
    "print(np.sort(Percentage_Expenditure_values))\n",
    "print(\"Outliers for Hepatitis B\")\n",
    "Hepatitis_B_indices, Hepatitis_B_values = find_outliers_tukey(df1['Hepatitis B'])\n",
    "print(np.sort(Hepatitis_B_values))\n",
    "print(\"Outliers for Measles\")\n",
    "Measles_indices, Measles_values = find_outliers_tukey(df1[\"Measles \"])\n",
    "print(np.sort(Measles_values))\n",
    "print(\"Outliers for BMI\")\n",
    "BMI_indices, BMI_values = find_outliers_tukey(df1[' BMI '])\n",
    "print(np.sort(BMI_values))\n",
    "print(\"Outliers for Under Five Death\")\n",
    "Under_Five_Death_indices, Under_Five_Death_values = find_outliers_tukey(df1['under-five deaths '])\n",
    "print(np.sort(Under_Five_Death_values))\n",
    "print(\"Outliers for Polio\")\n",
    "Polio_indices, Polio_values = find_outliers_tukey(df1['Polio'])\n",
    "print(np.sort(Polio_values))\n",
    "print(\"Outliers for Total Expenditure\")\n",
    "Total_Expenditure_indices, Total_Expenditure_values = find_outliers_tukey(df1['Total expenditure'])\n",
    "print(np.sort(Total_Expenditure_values))\n",
    "print(\"Outliers for Diphtheria\")\n",
    "Diphtheria_indices, Diphtheria_values = find_outliers_tukey(df1['Diphtheria '])\n",
    "print(np.sort(Diphtheria_values))\n",
    "print(\"Outliers for Hiv_Aids\")\n",
    "Hiv_Aids_indices, Hiv_Aids_values = find_outliers_tukey(df1[' HIV/AIDS'])\n",
    "print(np.sort(Hiv_Aids_values))\n",
    "print(\"Outliers for GDP\")\n",
    "GDP_indices, GDP_values = find_outliers_tukey(df1['GDP'])\n",
    "print(np.sort(GDP_values))\n",
    "print(\"Outliers for Population\")\n",
    "Population_indices, Population_values = find_outliers_tukey(df1['Population'])\n",
    "print(np.sort(Population_values))\n",
    "print(\"Outliers for Thin_1_19_Years\")\n",
    "Thin_1_19_Years_indices, Thin_1_19_Years_values = find_outliers_tukey(df1[' thinness  1-19 years'])\n",
    "print(np.sort(Thin_1_19_Years_values))\n",
    "print(\"Outliers for Thin_5_9_Year\")\n",
    "Thin_5_9_Year_indices, Thin_5_9_Year_values = find_outliers_tukey(df1[' thinness 5-9 years'])\n",
    "print(np.sort(Thin_5_9_Year_values))\n",
    "print(\"Outliers for Income Comp Resource\")\n",
    "Income_Comp_Resource_indices, Income_Comp_Resource_values = find_outliers_tukey(df1['Income composition of resources'])\n",
    "print(np.sort(Income_Comp_Resource_values))\n",
    "print(\"Outliers for Schooling\")\n",
    "Schooling_indices, Schooling_values = find_outliers_tukey(df1['Schooling'])\n",
    "print(np.sort(Schooling_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Life expectancy ', 'Adult Mortality', 'infant deaths',\n",
       "       'Alcohol', 'percentage expenditure', 'Hepatitis B', 'Measles ', ' BMI ',\n",
       "       'under-five deaths ',\n",
       "       ...\n",
       "       'Country_Turkmenistan', 'Country_Uganda', 'Country_Ukraine',\n",
       "       'Country_Uruguay', 'Country_Uzbekistan', 'Country_Vanuatu',\n",
       "       'Country_Zambia', 'Country_Zimbabwe', 'Status_Developed',\n",
       "       'Status_Developing'],\n",
       "      dtype='object', length=155)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_enco=pd.get_dummies(df1.Country,prefix='Country').iloc[:,0:]\n",
    "df1=pd.concat([df1,Country_enco],axis=1)\n",
    "df1=df1.drop('Country',axis=1)\n",
    "Status_enco=pd.get_dummies(df1.Status,prefix='Status').iloc[:,0:]\n",
    "df1=pd.concat([df1,Status_enco],axis=1)\n",
    "df1=df1.drop('Status',axis=1)\n",
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Year',\n",
       " 'Life expectancy ',\n",
       " 'Adult Mortality',\n",
       " 'infant deaths',\n",
       " 'Alcohol',\n",
       " 'percentage expenditure',\n",
       " 'Hepatitis B',\n",
       " 'Measles ',\n",
       " ' BMI ',\n",
       " 'under-five deaths ',\n",
       " 'Polio',\n",
       " 'Total expenditure',\n",
       " 'Diphtheria ',\n",
       " ' HIV/AIDS',\n",
       " 'GDP',\n",
       " 'Population',\n",
       " ' thinness  1-19 years',\n",
       " ' thinness 5-9 years',\n",
       " 'Income composition of resources',\n",
       " 'Schooling',\n",
       " 'Country_Afghanistan',\n",
       " 'Country_Albania',\n",
       " 'Country_Algeria',\n",
       " 'Country_Angola',\n",
       " 'Country_Argentina',\n",
       " 'Country_Armenia',\n",
       " 'Country_Australia',\n",
       " 'Country_Austria',\n",
       " 'Country_Azerbaijan',\n",
       " 'Country_Bangladesh',\n",
       " 'Country_Belarus',\n",
       " 'Country_Belgium',\n",
       " 'Country_Belize',\n",
       " 'Country_Benin',\n",
       " 'Country_Bhutan',\n",
       " 'Country_Bosnia and Herzegovina',\n",
       " 'Country_Botswana',\n",
       " 'Country_Brazil',\n",
       " 'Country_Bulgaria',\n",
       " 'Country_Burkina Faso',\n",
       " 'Country_Burundi',\n",
       " 'Country_Cabo Verde',\n",
       " 'Country_Cambodia',\n",
       " 'Country_Cameroon',\n",
       " 'Country_Canada',\n",
       " 'Country_Central African Republic',\n",
       " 'Country_Chad',\n",
       " 'Country_Chile',\n",
       " 'Country_China',\n",
       " 'Country_Colombia',\n",
       " 'Country_Comoros',\n",
       " 'Country_Costa Rica',\n",
       " 'Country_Croatia',\n",
       " 'Country_Cyprus',\n",
       " 'Country_Djibouti',\n",
       " 'Country_Dominican Republic',\n",
       " 'Country_Ecuador',\n",
       " 'Country_El Salvador',\n",
       " 'Country_Equatorial Guinea',\n",
       " 'Country_Eritrea',\n",
       " 'Country_Estonia',\n",
       " 'Country_Ethiopia',\n",
       " 'Country_Fiji',\n",
       " 'Country_France',\n",
       " 'Country_Gabon',\n",
       " 'Country_Georgia',\n",
       " 'Country_Germany',\n",
       " 'Country_Ghana',\n",
       " 'Country_Greece',\n",
       " 'Country_Guatemala',\n",
       " 'Country_Guinea',\n",
       " 'Country_Guinea-Bissau',\n",
       " 'Country_Guyana',\n",
       " 'Country_Haiti',\n",
       " 'Country_Honduras',\n",
       " 'Country_India',\n",
       " 'Country_Indonesia',\n",
       " 'Country_Iraq',\n",
       " 'Country_Ireland',\n",
       " 'Country_Israel',\n",
       " 'Country_Italy',\n",
       " 'Country_Jamaica',\n",
       " 'Country_Jordan',\n",
       " 'Country_Kazakhstan',\n",
       " 'Country_Kenya',\n",
       " 'Country_Kiribati',\n",
       " 'Country_Latvia',\n",
       " 'Country_Lebanon',\n",
       " 'Country_Lesotho',\n",
       " 'Country_Liberia',\n",
       " 'Country_Lithuania',\n",
       " 'Country_Luxembourg',\n",
       " 'Country_Madagascar',\n",
       " 'Country_Malawi',\n",
       " 'Country_Malaysia',\n",
       " 'Country_Maldives',\n",
       " 'Country_Mali',\n",
       " 'Country_Malta',\n",
       " 'Country_Mauritania',\n",
       " 'Country_Mauritius',\n",
       " 'Country_Mexico',\n",
       " 'Country_Mongolia',\n",
       " 'Country_Montenegro',\n",
       " 'Country_Morocco',\n",
       " 'Country_Mozambique',\n",
       " 'Country_Myanmar',\n",
       " 'Country_Namibia',\n",
       " 'Country_Nepal',\n",
       " 'Country_Netherlands',\n",
       " 'Country_Nicaragua',\n",
       " 'Country_Niger',\n",
       " 'Country_Nigeria',\n",
       " 'Country_Pakistan',\n",
       " 'Country_Panama',\n",
       " 'Country_Papua New Guinea',\n",
       " 'Country_Paraguay',\n",
       " 'Country_Peru',\n",
       " 'Country_Philippines',\n",
       " 'Country_Poland',\n",
       " 'Country_Portugal',\n",
       " 'Country_Romania',\n",
       " 'Country_Russian Federation',\n",
       " 'Country_Rwanda',\n",
       " 'Country_Samoa',\n",
       " 'Country_Sao Tome and Principe',\n",
       " 'Country_Senegal',\n",
       " 'Country_Serbia',\n",
       " 'Country_Seychelles',\n",
       " 'Country_Sierra Leone',\n",
       " 'Country_Solomon Islands',\n",
       " 'Country_South Africa',\n",
       " 'Country_Spain',\n",
       " 'Country_Sri Lanka',\n",
       " 'Country_Suriname',\n",
       " 'Country_Swaziland',\n",
       " 'Country_Sweden',\n",
       " 'Country_Syrian Arab Republic',\n",
       " 'Country_Tajikistan',\n",
       " 'Country_Thailand',\n",
       " 'Country_Timor-Leste',\n",
       " 'Country_Togo',\n",
       " 'Country_Tonga',\n",
       " 'Country_Trinidad and Tobago',\n",
       " 'Country_Tunisia',\n",
       " 'Country_Turkey',\n",
       " 'Country_Turkmenistan',\n",
       " 'Country_Uganda',\n",
       " 'Country_Ukraine',\n",
       " 'Country_Uruguay',\n",
       " 'Country_Uzbekistan',\n",
       " 'Country_Vanuatu',\n",
       " 'Country_Zambia',\n",
       " 'Country_Zimbabwe',\n",
       " 'Status_Developed',\n",
       " 'Status_Developing']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_column = list(df1.columns) # Making a list out of column names\n",
    "len_feature = len(l_column) # Length of column vector list\n",
    "l_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = df[l_column[0:len_feature-2]]\n",
    "#y = df[l_column[len_feature-2]]\n",
    "X=df1.iloc[:,2:]\n",
    "Y=df1.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training feature set size: (1154, 153)\n",
      "Test feature set size: (495, 153)\n",
      "Training variable set size: (1154,)\n",
      "Test variable set size: (495,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SHALINI\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=123)\n",
    "print(\"Training feature set size:\",X_train.shape)\n",
    "print(\"Test feature set size:\",X_test.shape)\n",
    "print(\"Training variable set size:\",Y_train.shape)\n",
    "print(\"Test variable set size:\",Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept term of the linear model: 61.02166771233012\n",
      "The coefficients of the linear model: [-1.04758576e-03 -4.93079320e-01 -1.58691143e-01  4.88050173e-03\n",
      "  8.48132987e-03  4.64481021e-03  3.03619112e-03 -3.25830062e-01\n",
      " -3.38712499e-03  3.25093700e-03  5.89044252e-04 -3.60036485e-01\n",
      " -7.24907891e-06  3.07786019e-10 -3.18326004e-02  6.03633641e-02\n",
      "  3.31757319e+00  8.60124349e-01 -6.22624963e+00  5.01743280e+00\n",
      "  4.74002032e+00 -1.34409489e+01  3.67659693e+00  3.58840632e+00\n",
      " -9.79981296e-01  1.89758266e+00  3.95251422e+00  5.09857664e+00\n",
      " -1.32832566e+00  1.00168351e+00 -1.66968870e+00 -6.49855446e+00\n",
      " -1.44615496e+00  4.90576428e+00 -6.46921611e+00  3.46542038e+00\n",
      " -3.79537312e+00 -4.29627400e+00 -6.65885565e+00  2.11245745e+00\n",
      " -3.99862307e-02 -7.96138959e+00  1.00207472e+01 -8.71471288e+00\n",
      " -8.40004151e+00  8.22918526e+00  7.01490708e+00  5.34930349e+00\n",
      " -5.51124883e+00  7.79725220e+00 -1.65817438e+00  1.60373397e+00\n",
      "  2.47529894e-01  4.54480668e+00  5.90221795e+00  2.21358868e+00\n",
      " -5.87124213e+00  8.10019724e-01  1.00099335e+00 -4.68558600e-01\n",
      " -4.45935244e+00  1.17292911e+01 -2.96840105e+00  3.41416210e+00\n",
      "  3.23994755e+00 -3.77671771e+00  7.47166230e+00  6.33109022e+00\n",
      " -5.79207427e+00 -5.41379689e+00 -2.24396199e+00 -1.24281500e+00\n",
      "  6.33989280e+00 -7.35532570e-01 -4.75489255e-01  4.23039040e+00\n",
      "  3.74543197e+00  6.85823080e+00  4.23462613e+00  4.51718559e+00\n",
      "  2.86957749e+00 -2.83849398e+00 -5.47993658e+00 -4.84652154e+00\n",
      " -6.28115728e+00  3.18653468e+00 -9.32848614e+00 -2.81396714e+00\n",
      " -6.55963704e+00  3.50725753e+00 -1.95139245e+00 -9.70594909e+00\n",
      "  4.19087146e+00  3.61759090e+00 -6.89964524e+00  2.47949259e+00\n",
      " -6.75248566e-02  2.48545373e-01  7.44895054e+00 -4.47059296e+00\n",
      "  1.99852678e+00  5.39152436e+00 -5.54868888e+00 -5.01251251e-01\n",
      " -1.08585592e+00  1.64968266e-01 -3.32739106e-01  6.15890373e+00\n",
      "  1.44470462e+00 -8.21203683e+00  9.35439543e-01  6.53965358e+00\n",
      " -2.23936772e+00  4.75042359e+00  5.15994674e+00 -5.49848838e-02\n",
      " -1.75547165e+00  1.18891080e-01 -2.12748557e+00 -8.12777565e-01\n",
      " -2.51719165e+00  1.57085948e+00 -3.00727130e+00  6.99222943e-01\n",
      "  3.63377554e+00  1.29711484e+00 -1.49380973e+01  2.24893877e-01\n",
      " -3.62440199e+00  3.28424844e+00  3.46768908e+00  7.66796920e-01\n",
      " -2.92536363e+00  1.45299591e+00  6.16389187e+00 -1.12007704e-01\n",
      "  4.76788336e+00 -2.48492043e+00 -7.97206552e+00 -6.48364955e-01\n",
      "  7.69845703e-01  3.67954979e+00  4.71283458e+00 -4.22725906e-01\n",
      " -6.10626404e+00  9.30205917e-01  3.29170755e+00  2.57504192e-02\n",
      "  2.95334855e+00 -6.49071593e+00 -6.50059787e+00  3.07587190e+00\n",
      " -3.07587190e+00]\n"
     ]
    }
   ],
   "source": [
    "print(\"The intercept term of the linear model:\", lm.intercept_)\n",
    "print(\"The coefficients of the linear model:\", lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adult Mortality</th>\n",
       "      <td>-1.047586e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infant deaths</th>\n",
       "      <td>-4.930793e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcohol</th>\n",
       "      <td>-1.586911e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage expenditure</th>\n",
       "      <td>4.880502e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hepatitis B</th>\n",
       "      <td>8.481330e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Measles</th>\n",
       "      <td>4.644810e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>3.036191e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>under-five deaths</th>\n",
       "      <td>-3.258301e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polio</th>\n",
       "      <td>-3.387125e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total expenditure</th>\n",
       "      <td>3.250937e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diphtheria</th>\n",
       "      <td>5.890443e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <td>-3.600365e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>-7.249079e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>3.077860e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <td>-3.183260e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <td>6.036336e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income composition of resources</th>\n",
       "      <td>3.317573e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schooling</th>\n",
       "      <td>8.601243e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Afghanistan</th>\n",
       "      <td>-6.226250e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Albania</th>\n",
       "      <td>5.017433e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Algeria</th>\n",
       "      <td>4.740020e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Angola</th>\n",
       "      <td>-1.344095e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Argentina</th>\n",
       "      <td>3.676597e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Armenia</th>\n",
       "      <td>3.588406e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Australia</th>\n",
       "      <td>-9.799813e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Austria</th>\n",
       "      <td>1.897583e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Azerbaijan</th>\n",
       "      <td>3.952514e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Bangladesh</th>\n",
       "      <td>5.098577e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Belarus</th>\n",
       "      <td>-1.328326e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Belgium</th>\n",
       "      <td>1.001684e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Senegal</th>\n",
       "      <td>6.992229e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Serbia</th>\n",
       "      <td>3.633776e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Seychelles</th>\n",
       "      <td>1.297115e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Sierra Leone</th>\n",
       "      <td>-1.493810e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Solomon Islands</th>\n",
       "      <td>2.248939e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_South Africa</th>\n",
       "      <td>-3.624402e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Spain</th>\n",
       "      <td>3.284248e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Sri Lanka</th>\n",
       "      <td>3.467689e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Suriname</th>\n",
       "      <td>7.667969e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Swaziland</th>\n",
       "      <td>-2.925364e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Sweden</th>\n",
       "      <td>1.452996e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Syrian Arab Republic</th>\n",
       "      <td>6.163892e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Tajikistan</th>\n",
       "      <td>-1.120077e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Thailand</th>\n",
       "      <td>4.767883e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Timor-Leste</th>\n",
       "      <td>-2.484920e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Togo</th>\n",
       "      <td>-7.972066e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Tonga</th>\n",
       "      <td>-6.483650e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Trinidad and Tobago</th>\n",
       "      <td>7.698457e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Tunisia</th>\n",
       "      <td>3.679550e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Turkey</th>\n",
       "      <td>4.712835e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Turkmenistan</th>\n",
       "      <td>-4.227259e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Uganda</th>\n",
       "      <td>-6.106264e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Ukraine</th>\n",
       "      <td>9.302059e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Uruguay</th>\n",
       "      <td>3.291708e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Uzbekistan</th>\n",
       "      <td>2.575042e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Vanuatu</th>\n",
       "      <td>2.953349e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Zambia</th>\n",
       "      <td>-6.490716e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Zimbabwe</th>\n",
       "      <td>-6.500598e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status_Developed</th>\n",
       "      <td>3.075872e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status_Developing</th>\n",
       "      <td>-3.075872e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Coefficients\n",
       "Adult Mortality                 -1.047586e-03\n",
       "infant deaths                   -4.930793e-01\n",
       "Alcohol                         -1.586911e-01\n",
       "percentage expenditure           4.880502e-03\n",
       "Hepatitis B                      8.481330e-03\n",
       "Measles                          4.644810e-03\n",
       " BMI                             3.036191e-03\n",
       "under-five deaths               -3.258301e-01\n",
       "Polio                           -3.387125e-03\n",
       "Total expenditure                3.250937e-03\n",
       "Diphtheria                       5.890443e-04\n",
       " HIV/AIDS                       -3.600365e-01\n",
       "GDP                             -7.249079e-06\n",
       "Population                       3.077860e-10\n",
       " thinness  1-19 years           -3.183260e-02\n",
       " thinness 5-9 years              6.036336e-02\n",
       "Income composition of resources  3.317573e+00\n",
       "Schooling                        8.601243e-01\n",
       "Country_Afghanistan             -6.226250e+00\n",
       "Country_Albania                  5.017433e+00\n",
       "Country_Algeria                  4.740020e+00\n",
       "Country_Angola                  -1.344095e+01\n",
       "Country_Argentina                3.676597e+00\n",
       "Country_Armenia                  3.588406e+00\n",
       "Country_Australia               -9.799813e-01\n",
       "Country_Austria                  1.897583e+00\n",
       "Country_Azerbaijan               3.952514e+00\n",
       "Country_Bangladesh               5.098577e+00\n",
       "Country_Belarus                 -1.328326e+00\n",
       "Country_Belgium                  1.001684e+00\n",
       "...                                       ...\n",
       "Country_Senegal                  6.992229e-01\n",
       "Country_Serbia                   3.633776e+00\n",
       "Country_Seychelles               1.297115e+00\n",
       "Country_Sierra Leone            -1.493810e+01\n",
       "Country_Solomon Islands          2.248939e-01\n",
       "Country_South Africa            -3.624402e+00\n",
       "Country_Spain                    3.284248e+00\n",
       "Country_Sri Lanka                3.467689e+00\n",
       "Country_Suriname                 7.667969e-01\n",
       "Country_Swaziland               -2.925364e+00\n",
       "Country_Sweden                   1.452996e+00\n",
       "Country_Syrian Arab Republic     6.163892e+00\n",
       "Country_Tajikistan              -1.120077e-01\n",
       "Country_Thailand                 4.767883e+00\n",
       "Country_Timor-Leste             -2.484920e+00\n",
       "Country_Togo                    -7.972066e+00\n",
       "Country_Tonga                   -6.483650e-01\n",
       "Country_Trinidad and Tobago      7.698457e-01\n",
       "Country_Tunisia                  3.679550e+00\n",
       "Country_Turkey                   4.712835e+00\n",
       "Country_Turkmenistan            -4.227259e-01\n",
       "Country_Uganda                  -6.106264e+00\n",
       "Country_Ukraine                  9.302059e-01\n",
       "Country_Uruguay                  3.291708e+00\n",
       "Country_Uzbekistan               2.575042e-02\n",
       "Country_Vanuatu                  2.953349e+00\n",
       "Country_Zambia                  -6.490716e+00\n",
       "Country_Zimbabwe                -6.500598e+00\n",
       "Status_Developed                 3.075872e+00\n",
       "Status_Developing               -3.075872e+00\n",
       "\n",
       "[153 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = pd.DataFrame(data=lm.coef_, index=X_train.columns, columns=[\"Coefficients\"])\n",
    "#cdf=pd.concat([idf,cdf], axis=0)\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list assignment index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-706b9de569d8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msum_error\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdfN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mse\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Standard Error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m't-statistic'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Coefficients'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Standard Error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range"
     ]
    }
   ],
   "source": [
    "n=X_train.shape[0]\n",
    "k=X_train.shape[1]\n",
    "dfN = n-k\n",
    "train_pred=lm.predict(X_train)\n",
    "train_error = np.square(train_pred - Y_train)\n",
    "sum_error=np.sum(train_error)\n",
    "se=[0,0,0,0,0]\n",
    "for i in range(k):\n",
    "    r = (sum_error/dfN)\n",
    "    r = r/np.sum(np.square(X_train[list(X_train.columns)[i]]-X_train[list(X_train.columns)[i]].mean()))\n",
    "    se[i]=np.sqrt(r)\n",
    "cdf['Standard Error']=se\n",
    "cdf['t-statistic']=cdf['Coefficients']/cdf['Standard Error']\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = lm.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.014342584195109"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(Y_train,train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0964353986015976"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(Y_train,train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7361862181791183"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(Y_train,train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value of this fit: 0.96\n"
     ]
    }
   ],
   "source": [
    "print(\"R-squared value of this fit:\",round(metrics.r2_score(Y_train,train_pred),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of the predicted object: <class 'numpy.ndarray'>\n",
      "Size of the predicted object: (495,)\n"
     ]
    }
   ],
   "source": [
    "predictions = lm.predict(X_test)\n",
    "print (\"Type of the predicted object:\", type(predictions))\n",
    "print (\"Size of the predicted object:\", predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error (MAE): 1.0964353986015976\n",
      "Mean square error (MSE): 3.014342584195109\n",
      "Root mean square error (RMSE): 1.7361862181791183\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean absolute error (MAE):\", metrics.mean_absolute_error(Y_train,train_pred))\n",
    "print(\"Mean square error (MSE):\", metrics.mean_squared_error(Y_train,train_pred))\n",
    "print(\"Root mean square error (RMSE):\", np.sqrt(metrics.mean_squared_error(Y_train,train_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error (MAE): 1.1155453568096494\n",
      "Mean square error (MSE): 2.7408259626890317\n",
      "Root mean square error (RMSE): 1.655544008079831\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean absolute error (MAE):\", metrics.mean_absolute_error(Y_test,predictions))\n",
    "print(\"Mean square error (MSE):\", metrics.mean_squared_error(Y_test,predictions))\n",
    "print(\"Root mean square error (RMSE):\", np.sqrt(metrics.mean_squared_error(Y_test,predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value of predictions: 0.966\n"
     ]
    }
   ],
   "source": [
    "print(\"R-squared value of predictions:\",round(metrics.r2_score(Y_test,predictions),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true,y_pred):\n",
    "    y_true,y_pred=np.array(y_true),np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true-y_pred)/y_true))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE 1.6177227988146332\n"
     ]
    }
   ],
   "source": [
    "print('MAPE',mean_absolute_percentage_error(Y_train,train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE 1.6353676258283383\n"
     ]
    }
   ],
   "source": [
    "print('MAPE',mean_absolute_percentage_error(Y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'df1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-10a2469ff952>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#df2=df1['infant deaths']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'df1'"
     ]
    }
   ],
   "source": [
    "#df2=df1['infant deaths']\n",
    "sns.pairplot.df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Life expectancy ', 'Adult Mortality', 'infant deaths',\n",
       "       'Alcohol', 'percentage expenditure', 'Hepatitis B', 'Measles ', ' BMI ',\n",
       "       'under-five deaths ',\n",
       "       ...\n",
       "       'Country_Turkmenistan', 'Country_Uganda', 'Country_Ukraine',\n",
       "       'Country_Uruguay', 'Country_Uzbekistan', 'Country_Vanuatu',\n",
       "       'Country_Zambia', 'Country_Zimbabwe', 'Status_Developed',\n",
       "       'Status_Developing'],\n",
       "      dtype='object', length=155)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x521a518>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(df1.corr(),annot=True,linewidths=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>...</th>\n",
       "      <th>Country_Turkmenistan</th>\n",
       "      <th>Country_Uganda</th>\n",
       "      <th>Country_Ukraine</th>\n",
       "      <th>Country_Uruguay</th>\n",
       "      <th>Country_Uzbekistan</th>\n",
       "      <th>Country_Vanuatu</th>\n",
       "      <th>Country_Zambia</th>\n",
       "      <th>Country_Zimbabwe</th>\n",
       "      <th>Status_Developed</th>\n",
       "      <th>Status_Developing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050771</td>\n",
       "      <td>-0.037092</td>\n",
       "      <td>-0.001142</td>\n",
       "      <td>-0.113365</td>\n",
       "      <td>0.110494</td>\n",
       "      <td>0.114897</td>\n",
       "      <td>-0.048340</td>\n",
       "      <td>0.005739</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003479</td>\n",
       "      <td>0.003479</td>\n",
       "      <td>-0.019707</td>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.003479</td>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.031720</td>\n",
       "      <td>-0.019707</td>\n",
       "      <td>-0.034138</td>\n",
       "      <td>0.034138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life expectancy</th>\n",
       "      <td>0.050771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.702523</td>\n",
       "      <td>-0.443694</td>\n",
       "      <td>0.402718</td>\n",
       "      <td>0.312599</td>\n",
       "      <td>0.199935</td>\n",
       "      <td>-0.163407</td>\n",
       "      <td>0.542042</td>\n",
       "      <td>-0.479005</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047665</td>\n",
       "      <td>-0.129614</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.073116</td>\n",
       "      <td>-0.012733</td>\n",
       "      <td>0.022273</td>\n",
       "      <td>-0.107050</td>\n",
       "      <td>-0.216980</td>\n",
       "      <td>0.442798</td>\n",
       "      <td>-0.442798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adult Mortality</th>\n",
       "      <td>-0.037092</td>\n",
       "      <td>-0.702523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.316530</td>\n",
       "      <td>-0.175535</td>\n",
       "      <td>-0.156047</td>\n",
       "      <td>-0.105225</td>\n",
       "      <td>0.125084</td>\n",
       "      <td>-0.351542</td>\n",
       "      <td>0.348627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032306</td>\n",
       "      <td>0.067447</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>-0.036723</td>\n",
       "      <td>0.011615</td>\n",
       "      <td>-0.016838</td>\n",
       "      <td>0.125818</td>\n",
       "      <td>0.231425</td>\n",
       "      <td>-0.278173</td>\n",
       "      <td>0.278173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infant deaths</th>\n",
       "      <td>-0.001142</td>\n",
       "      <td>-0.443694</td>\n",
       "      <td>0.316530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.328625</td>\n",
       "      <td>-0.206979</td>\n",
       "      <td>-0.146108</td>\n",
       "      <td>0.303586</td>\n",
       "      <td>-0.342299</td>\n",
       "      <td>0.982329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>-0.099774</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>-0.144838</td>\n",
       "      <td>0.065612</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>-0.350197</td>\n",
       "      <td>0.350197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcohol</th>\n",
       "      <td>-0.113365</td>\n",
       "      <td>0.402718</td>\n",
       "      <td>-0.175535</td>\n",
       "      <td>-0.328625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242047</td>\n",
       "      <td>0.109889</td>\n",
       "      <td>-0.039255</td>\n",
       "      <td>0.353396</td>\n",
       "      <td>-0.330412</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041472</td>\n",
       "      <td>0.069437</td>\n",
       "      <td>0.067462</td>\n",
       "      <td>0.038998</td>\n",
       "      <td>-0.064573</td>\n",
       "      <td>-0.088642</td>\n",
       "      <td>-0.046933</td>\n",
       "      <td>-0.001218</td>\n",
       "      <td>0.607782</td>\n",
       "      <td>-0.607782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage expenditure</th>\n",
       "      <td>0.110494</td>\n",
       "      <td>0.312599</td>\n",
       "      <td>-0.156047</td>\n",
       "      <td>-0.206979</td>\n",
       "      <td>0.242047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110430</td>\n",
       "      <td>-0.098559</td>\n",
       "      <td>0.246558</td>\n",
       "      <td>-0.211914</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015324</td>\n",
       "      <td>-0.003569</td>\n",
       "      <td>-0.023760</td>\n",
       "      <td>0.018240</td>\n",
       "      <td>-0.054556</td>\n",
       "      <td>0.025231</td>\n",
       "      <td>-0.010388</td>\n",
       "      <td>-0.122935</td>\n",
       "      <td>0.183455</td>\n",
       "      <td>-0.183455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hepatitis B</th>\n",
       "      <td>0.114897</td>\n",
       "      <td>0.199935</td>\n",
       "      <td>-0.105225</td>\n",
       "      <td>-0.146108</td>\n",
       "      <td>0.109889</td>\n",
       "      <td>0.110430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.093302</td>\n",
       "      <td>0.143302</td>\n",
       "      <td>-0.140176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062463</td>\n",
       "      <td>-0.058889</td>\n",
       "      <td>-0.083912</td>\n",
       "      <td>0.056330</td>\n",
       "      <td>0.056301</td>\n",
       "      <td>-0.088404</td>\n",
       "      <td>-0.010124</td>\n",
       "      <td>-0.036499</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>-0.140351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Measles</th>\n",
       "      <td>-0.048340</td>\n",
       "      <td>-0.163407</td>\n",
       "      <td>0.125084</td>\n",
       "      <td>0.303586</td>\n",
       "      <td>-0.039255</td>\n",
       "      <td>-0.098559</td>\n",
       "      <td>-0.093302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155825</td>\n",
       "      <td>0.303885</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039032</td>\n",
       "      <td>0.056966</td>\n",
       "      <td>0.058952</td>\n",
       "      <td>-0.096332</td>\n",
       "      <td>0.010265</td>\n",
       "      <td>-0.058267</td>\n",
       "      <td>0.081803</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>0.030122</td>\n",
       "      <td>-0.030122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.005739</td>\n",
       "      <td>0.542042</td>\n",
       "      <td>-0.351542</td>\n",
       "      <td>-0.342299</td>\n",
       "      <td>0.353396</td>\n",
       "      <td>0.246558</td>\n",
       "      <td>0.143302</td>\n",
       "      <td>-0.155825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.362092</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003463</td>\n",
       "      <td>-0.101414</td>\n",
       "      <td>0.058566</td>\n",
       "      <td>0.068205</td>\n",
       "      <td>-0.018601</td>\n",
       "      <td>0.026804</td>\n",
       "      <td>-0.083966</td>\n",
       "      <td>-0.065184</td>\n",
       "      <td>0.298380</td>\n",
       "      <td>-0.298380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>under-five deaths</th>\n",
       "      <td>0.001251</td>\n",
       "      <td>-0.479005</td>\n",
       "      <td>0.348627</td>\n",
       "      <td>0.982329</td>\n",
       "      <td>-0.330412</td>\n",
       "      <td>-0.211914</td>\n",
       "      <td>-0.140176</td>\n",
       "      <td>0.303885</td>\n",
       "      <td>-0.362092</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>-0.098097</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>-0.143476</td>\n",
       "      <td>0.068005</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>-0.357250</td>\n",
       "      <td>0.357250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polio</th>\n",
       "      <td>-0.016699</td>\n",
       "      <td>0.327294</td>\n",
       "      <td>-0.199853</td>\n",
       "      <td>-0.193578</td>\n",
       "      <td>0.240315</td>\n",
       "      <td>0.122143</td>\n",
       "      <td>0.463331</td>\n",
       "      <td>-0.082422</td>\n",
       "      <td>0.186268</td>\n",
       "      <td>-0.191011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046335</td>\n",
       "      <td>-0.070985</td>\n",
       "      <td>-0.000418</td>\n",
       "      <td>0.045402</td>\n",
       "      <td>0.059167</td>\n",
       "      <td>-0.073844</td>\n",
       "      <td>-0.083404</td>\n",
       "      <td>-0.037416</td>\n",
       "      <td>0.201917</td>\n",
       "      <td>-0.201917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total expenditure</th>\n",
       "      <td>0.059493</td>\n",
       "      <td>0.174718</td>\n",
       "      <td>-0.085227</td>\n",
       "      <td>-0.163771</td>\n",
       "      <td>0.214885</td>\n",
       "      <td>0.146727</td>\n",
       "      <td>0.113327</td>\n",
       "      <td>-0.076945</td>\n",
       "      <td>0.189469</td>\n",
       "      <td>-0.175422</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.127694</td>\n",
       "      <td>0.109396</td>\n",
       "      <td>0.034237</td>\n",
       "      <td>0.116460</td>\n",
       "      <td>-0.010223</td>\n",
       "      <td>-0.084498</td>\n",
       "      <td>-0.028405</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>0.192538</td>\n",
       "      <td>-0.192538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diphtheria</th>\n",
       "      <td>0.029641</td>\n",
       "      <td>0.341331</td>\n",
       "      <td>-0.191429</td>\n",
       "      <td>-0.152680</td>\n",
       "      <td>0.242951</td>\n",
       "      <td>0.151581</td>\n",
       "      <td>0.588990</td>\n",
       "      <td>-0.096865</td>\n",
       "      <td>0.176295</td>\n",
       "      <td>-0.153218</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049898</td>\n",
       "      <td>-0.076610</td>\n",
       "      <td>-0.048952</td>\n",
       "      <td>0.020333</td>\n",
       "      <td>0.058480</td>\n",
       "      <td>-0.112908</td>\n",
       "      <td>-0.029891</td>\n",
       "      <td>-0.043327</td>\n",
       "      <td>0.201654</td>\n",
       "      <td>-0.201654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <td>-0.123405</td>\n",
       "      <td>-0.592236</td>\n",
       "      <td>0.550691</td>\n",
       "      <td>0.214648</td>\n",
       "      <td>-0.027113</td>\n",
       "      <td>-0.112271</td>\n",
       "      <td>-0.094802</td>\n",
       "      <td>0.097443</td>\n",
       "      <td>-0.210897</td>\n",
       "      <td>0.243313</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027847</td>\n",
       "      <td>0.080856</td>\n",
       "      <td>-0.022622</td>\n",
       "      <td>-0.029931</td>\n",
       "      <td>-0.026028</td>\n",
       "      <td>-0.029931</td>\n",
       "      <td>0.097742</td>\n",
       "      <td>0.356143</td>\n",
       "      <td>-0.129555</td>\n",
       "      <td>0.129555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>0.086455</td>\n",
       "      <td>0.471736</td>\n",
       "      <td>-0.260614</td>\n",
       "      <td>-0.319806</td>\n",
       "      <td>0.363858</td>\n",
       "      <td>0.711232</td>\n",
       "      <td>0.149114</td>\n",
       "      <td>-0.116230</td>\n",
       "      <td>0.382356</td>\n",
       "      <td>-0.326580</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007515</td>\n",
       "      <td>-0.097368</td>\n",
       "      <td>-0.024721</td>\n",
       "      <td>0.032108</td>\n",
       "      <td>-0.080485</td>\n",
       "      <td>0.047822</td>\n",
       "      <td>-0.019979</td>\n",
       "      <td>-0.104169</td>\n",
       "      <td>0.277297</td>\n",
       "      <td>-0.277297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>0.012567</td>\n",
       "      <td>-0.022305</td>\n",
       "      <td>-0.015012</td>\n",
       "      <td>0.149754</td>\n",
       "      <td>-0.028880</td>\n",
       "      <td>-0.027590</td>\n",
       "      <td>-0.129723</td>\n",
       "      <td>0.087762</td>\n",
       "      <td>-0.081416</td>\n",
       "      <td>0.150614</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015425</td>\n",
       "      <td>0.002964</td>\n",
       "      <td>-0.005825</td>\n",
       "      <td>-0.016766</td>\n",
       "      <td>-0.017413</td>\n",
       "      <td>-0.019756</td>\n",
       "      <td>-0.008112</td>\n",
       "      <td>-0.009725</td>\n",
       "      <td>-0.034790</td>\n",
       "      <td>0.034790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <td>0.019757</td>\n",
       "      <td>-0.457838</td>\n",
       "      <td>0.272230</td>\n",
       "      <td>0.304793</td>\n",
       "      <td>-0.403755</td>\n",
       "      <td>-0.197801</td>\n",
       "      <td>-0.129406</td>\n",
       "      <td>0.230897</td>\n",
       "      <td>-0.547018</td>\n",
       "      <td>0.324214</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030063</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>-0.047039</td>\n",
       "      <td>-0.067599</td>\n",
       "      <td>-0.033643</td>\n",
       "      <td>-0.068294</td>\n",
       "      <td>0.031418</td>\n",
       "      <td>0.047012</td>\n",
       "      <td>-0.308005</td>\n",
       "      <td>0.308005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <td>0.014122</td>\n",
       "      <td>-0.457508</td>\n",
       "      <td>0.286723</td>\n",
       "      <td>0.307602</td>\n",
       "      <td>-0.386208</td>\n",
       "      <td>-0.209594</td>\n",
       "      <td>-0.133251</td>\n",
       "      <td>0.240425</td>\n",
       "      <td>-0.554094</td>\n",
       "      <td>0.326861</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030069</td>\n",
       "      <td>0.024760</td>\n",
       "      <td>-0.045605</td>\n",
       "      <td>-0.069082</td>\n",
       "      <td>-0.033606</td>\n",
       "      <td>-0.070181</td>\n",
       "      <td>0.027740</td>\n",
       "      <td>0.044873</td>\n",
       "      <td>-0.307279</td>\n",
       "      <td>0.307279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income composition of resources</th>\n",
       "      <td>0.122892</td>\n",
       "      <td>0.721083</td>\n",
       "      <td>-0.442203</td>\n",
       "      <td>-0.327805</td>\n",
       "      <td>0.561074</td>\n",
       "      <td>0.326441</td>\n",
       "      <td>0.184921</td>\n",
       "      <td>-0.113389</td>\n",
       "      <td>0.510505</td>\n",
       "      <td>-0.342213</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.206504</td>\n",
       "      <td>-0.088083</td>\n",
       "      <td>0.043404</td>\n",
       "      <td>0.069193</td>\n",
       "      <td>0.005501</td>\n",
       "      <td>-0.146266</td>\n",
       "      <td>-0.046538</td>\n",
       "      <td>-0.103098</td>\n",
       "      <td>0.463615</td>\n",
       "      <td>-0.463615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schooling</th>\n",
       "      <td>0.088732</td>\n",
       "      <td>0.727630</td>\n",
       "      <td>-0.421171</td>\n",
       "      <td>-0.425840</td>\n",
       "      <td>0.616975</td>\n",
       "      <td>0.351102</td>\n",
       "      <td>0.215182</td>\n",
       "      <td>-0.115381</td>\n",
       "      <td>0.554844</td>\n",
       "      <td>-0.448940</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052163</td>\n",
       "      <td>-0.045783</td>\n",
       "      <td>0.083660</td>\n",
       "      <td>0.106060</td>\n",
       "      <td>-0.011431</td>\n",
       "      <td>-0.053710</td>\n",
       "      <td>-0.012295</td>\n",
       "      <td>-0.079767</td>\n",
       "      <td>0.512543</td>\n",
       "      <td>-0.512543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Afghanistan</th>\n",
       "      <td>-0.008248</td>\n",
       "      <td>-0.125034</td>\n",
       "      <td>0.079685</td>\n",
       "      <td>0.083145</td>\n",
       "      <td>-0.111047</td>\n",
       "      <td>-0.071252</td>\n",
       "      <td>-0.056672</td>\n",
       "      <td>0.059655</td>\n",
       "      <td>-0.113328</td>\n",
       "      <td>0.086178</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008824</td>\n",
       "      <td>-0.008824</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.008824</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.007732</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.041051</td>\n",
       "      <td>0.041051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Albania</th>\n",
       "      <td>-0.008248</td>\n",
       "      <td>0.065890</td>\n",
       "      <td>-0.097310</td>\n",
       "      <td>-0.096288</td>\n",
       "      <td>0.007755</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.072632</td>\n",
       "      <td>-0.007059</td>\n",
       "      <td>0.054835</td>\n",
       "      <td>-0.093288</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008824</td>\n",
       "      <td>-0.008824</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.008824</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.007732</td>\n",
       "      <td>-0.009484</td>\n",
       "      <td>-0.041051</td>\n",
       "      <td>0.041051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Algeria</th>\n",
       "      <td>0.023252</td>\n",
       "      <td>0.045724</td>\n",
       "      <td>-0.042780</td>\n",
       "      <td>0.068835</td>\n",
       "      <td>-0.083127</td>\n",
       "      <td>0.015306</td>\n",
       "      <td>-0.008846</td>\n",
       "      <td>0.031770</td>\n",
       "      <td>0.044584</td>\n",
       "      <td>0.071346</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.006401</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.033986</td>\n",
       "      <td>0.033986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Angola</th>\n",
       "      <td>0.045440</td>\n",
       "      <td>-0.147892</td>\n",
       "      <td>0.108426</td>\n",
       "      <td>0.058649</td>\n",
       "      <td>0.053507</td>\n",
       "      <td>0.012702</td>\n",
       "      <td>-0.022416</td>\n",
       "      <td>0.042080</td>\n",
       "      <td>-0.069576</td>\n",
       "      <td>0.060788</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.005454</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.028957</td>\n",
       "      <td>0.028957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Argentina</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>0.060171</td>\n",
       "      <td>-0.048267</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>0.076825</td>\n",
       "      <td>0.056327</td>\n",
       "      <td>0.003796</td>\n",
       "      <td>-0.072761</td>\n",
       "      <td>0.073829</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Armenia</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.043627</td>\n",
       "      <td>-0.038916</td>\n",
       "      <td>-0.069732</td>\n",
       "      <td>-0.019756</td>\n",
       "      <td>-0.002199</td>\n",
       "      <td>0.029629</td>\n",
       "      <td>-0.003887</td>\n",
       "      <td>0.028615</td>\n",
       "      <td>-0.086753</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Australia</th>\n",
       "      <td>-0.007711</td>\n",
       "      <td>0.132632</td>\n",
       "      <td>-0.078141</td>\n",
       "      <td>-0.067346</td>\n",
       "      <td>0.129134</td>\n",
       "      <td>0.046006</td>\n",
       "      <td>0.051373</td>\n",
       "      <td>0.046390</td>\n",
       "      <td>0.078720</td>\n",
       "      <td>-0.064220</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008249</td>\n",
       "      <td>-0.008249</td>\n",
       "      <td>-0.008866</td>\n",
       "      <td>-0.008866</td>\n",
       "      <td>-0.008249</td>\n",
       "      <td>-0.008866</td>\n",
       "      <td>-0.007228</td>\n",
       "      <td>-0.008866</td>\n",
       "      <td>0.223123</td>\n",
       "      <td>-0.223123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Austria</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.132675</td>\n",
       "      <td>-0.078330</td>\n",
       "      <td>-0.144838</td>\n",
       "      <td>0.183224</td>\n",
       "      <td>0.061377</td>\n",
       "      <td>0.003926</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>0.046275</td>\n",
       "      <td>-0.143476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>0.231025</td>\n",
       "      <td>-0.231025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Azerbaijan</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>0.018690</td>\n",
       "      <td>-0.034419</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>-0.076966</td>\n",
       "      <td>0.011314</td>\n",
       "      <td>-0.079517</td>\n",
       "      <td>-0.020221</td>\n",
       "      <td>0.023829</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Bangladesh</th>\n",
       "      <td>0.013817</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>-0.022246</td>\n",
       "      <td>0.071918</td>\n",
       "      <td>-0.096145</td>\n",
       "      <td>-0.034271</td>\n",
       "      <td>-0.010763</td>\n",
       "      <td>0.051600</td>\n",
       "      <td>-0.102694</td>\n",
       "      <td>0.074541</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.006688</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.035508</td>\n",
       "      <td>0.035508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Senegal</th>\n",
       "      <td>0.023252</td>\n",
       "      <td>-0.051442</td>\n",
       "      <td>-0.001806</td>\n",
       "      <td>0.068835</td>\n",
       "      <td>-0.086087</td>\n",
       "      <td>-0.111034</td>\n",
       "      <td>0.022296</td>\n",
       "      <td>0.057140</td>\n",
       "      <td>-0.083376</td>\n",
       "      <td>0.071346</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.006401</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.033986</td>\n",
       "      <td>0.033986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Serbia</th>\n",
       "      <td>0.023252</td>\n",
       "      <td>0.045554</td>\n",
       "      <td>-0.025415</td>\n",
       "      <td>-0.059642</td>\n",
       "      <td>0.094816</td>\n",
       "      <td>0.037482</td>\n",
       "      <td>0.035975</td>\n",
       "      <td>-0.004875</td>\n",
       "      <td>0.075782</td>\n",
       "      <td>-0.074200</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.006401</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.033986</td>\n",
       "      <td>0.033986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Seychelles</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.032878</td>\n",
       "      <td>-0.005722</td>\n",
       "      <td>-0.144838</td>\n",
       "      <td>0.039679</td>\n",
       "      <td>0.016764</td>\n",
       "      <td>0.073049</td>\n",
       "      <td>-0.089686</td>\n",
       "      <td>-0.050079</td>\n",
       "      <td>-0.143476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Sierra Leone</th>\n",
       "      <td>0.045440</td>\n",
       "      <td>-0.165756</td>\n",
       "      <td>0.093098</td>\n",
       "      <td>0.058649</td>\n",
       "      <td>-0.036566</td>\n",
       "      <td>-0.073118</td>\n",
       "      <td>0.010658</td>\n",
       "      <td>0.060243</td>\n",
       "      <td>-0.073023</td>\n",
       "      <td>0.060788</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.005454</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.028957</td>\n",
       "      <td>0.028957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Solomon Islands</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>-0.018401</td>\n",
       "      <td>-0.023415</td>\n",
       "      <td>-0.144838</td>\n",
       "      <td>-0.088784</td>\n",
       "      <td>-0.046464</td>\n",
       "      <td>-0.007053</td>\n",
       "      <td>-0.096332</td>\n",
       "      <td>0.015969</td>\n",
       "      <td>-0.143476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_South Africa</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>-0.132508</td>\n",
       "      <td>0.191348</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>0.072679</td>\n",
       "      <td>0.047879</td>\n",
       "      <td>-0.049725</td>\n",
       "      <td>0.093392</td>\n",
       "      <td>-0.004085</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Spain</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.138559</td>\n",
       "      <td>-0.079605</td>\n",
       "      <td>-0.019661</td>\n",
       "      <td>0.130608</td>\n",
       "      <td>0.047071</td>\n",
       "      <td>0.048594</td>\n",
       "      <td>0.070432</td>\n",
       "      <td>0.097088</td>\n",
       "      <td>-0.033811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>0.231025</td>\n",
       "      <td>-0.231025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Sri Lanka</th>\n",
       "      <td>0.013817</td>\n",
       "      <td>0.040543</td>\n",
       "      <td>-0.010627</td>\n",
       "      <td>0.071918</td>\n",
       "      <td>-0.049081</td>\n",
       "      <td>-0.025858</td>\n",
       "      <td>0.035787</td>\n",
       "      <td>0.074547</td>\n",
       "      <td>-0.089724</td>\n",
       "      <td>0.066093</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.006688</td>\n",
       "      <td>-0.008203</td>\n",
       "      <td>-0.035508</td>\n",
       "      <td>0.035508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Suriname</th>\n",
       "      <td>0.031720</td>\n",
       "      <td>0.015434</td>\n",
       "      <td>-0.018902</td>\n",
       "      <td>-0.118079</td>\n",
       "      <td>0.014385</td>\n",
       "      <td>0.057221</td>\n",
       "      <td>0.017340</td>\n",
       "      <td>-0.078535</td>\n",
       "      <td>0.064438</td>\n",
       "      <td>-0.116968</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.006101</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.032394</td>\n",
       "      <td>0.032394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Swaziland</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>-0.201364</td>\n",
       "      <td>0.128887</td>\n",
       "      <td>0.055445</td>\n",
       "      <td>-0.001662</td>\n",
       "      <td>0.005213</td>\n",
       "      <td>0.004675</td>\n",
       "      <td>-0.001470</td>\n",
       "      <td>-0.063469</td>\n",
       "      <td>0.068290</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Sweden</th>\n",
       "      <td>0.056226</td>\n",
       "      <td>0.070639</td>\n",
       "      <td>-0.043975</td>\n",
       "      <td>-0.074543</td>\n",
       "      <td>0.034484</td>\n",
       "      <td>0.027288</td>\n",
       "      <td>-0.042320</td>\n",
       "      <td>0.065869</td>\n",
       "      <td>0.050430</td>\n",
       "      <td>-0.073842</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004396</td>\n",
       "      <td>-0.004396</td>\n",
       "      <td>-0.004725</td>\n",
       "      <td>-0.004725</td>\n",
       "      <td>-0.004396</td>\n",
       "      <td>-0.004725</td>\n",
       "      <td>-0.003852</td>\n",
       "      <td>-0.004725</td>\n",
       "      <td>0.118901</td>\n",
       "      <td>-0.118901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Syrian Arab Republic</th>\n",
       "      <td>-0.074162</td>\n",
       "      <td>0.030648</td>\n",
       "      <td>-0.021160</td>\n",
       "      <td>0.058649</td>\n",
       "      <td>-0.059317</td>\n",
       "      <td>0.038852</td>\n",
       "      <td>-0.043215</td>\n",
       "      <td>0.042080</td>\n",
       "      <td>0.029642</td>\n",
       "      <td>0.060788</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.005454</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>-0.028957</td>\n",
       "      <td>0.028957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Tajikistan</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>-0.024819</td>\n",
       "      <td>0.005539</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>-0.093973</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.015047</td>\n",
       "      <td>-0.050059</td>\n",
       "      <td>-0.011622</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Thailand</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.039850</td>\n",
       "      <td>-0.005365</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>0.038014</td>\n",
       "      <td>0.027188</td>\n",
       "      <td>0.066062</td>\n",
       "      <td>0.047472</td>\n",
       "      <td>-0.084105</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Timor-Leste</th>\n",
       "      <td>0.050481</td>\n",
       "      <td>-0.016033</td>\n",
       "      <td>-0.003612</td>\n",
       "      <td>0.003663</td>\n",
       "      <td>-0.071676</td>\n",
       "      <td>-0.074309</td>\n",
       "      <td>-0.008208</td>\n",
       "      <td>0.061408</td>\n",
       "      <td>-0.073682</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005820</td>\n",
       "      <td>-0.005820</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.005820</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.005100</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.027078</td>\n",
       "      <td>0.027078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Togo</th>\n",
       "      <td>0.050481</td>\n",
       "      <td>-0.083278</td>\n",
       "      <td>0.054018</td>\n",
       "      <td>0.054845</td>\n",
       "      <td>-0.061325</td>\n",
       "      <td>-0.016050</td>\n",
       "      <td>-0.010758</td>\n",
       "      <td>0.039350</td>\n",
       "      <td>-0.070281</td>\n",
       "      <td>0.056845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005820</td>\n",
       "      <td>-0.005820</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.005820</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.005100</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>-0.027078</td>\n",
       "      <td>0.027078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Tonga</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.034476</td>\n",
       "      <td>-0.029687</td>\n",
       "      <td>-0.144838</td>\n",
       "      <td>-0.080063</td>\n",
       "      <td>0.008869</td>\n",
       "      <td>-0.014540</td>\n",
       "      <td>-0.093915</td>\n",
       "      <td>0.116430</td>\n",
       "      <td>-0.143476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Trinidad and Tobago</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>0.020873</td>\n",
       "      <td>0.001051</td>\n",
       "      <td>-0.134754</td>\n",
       "      <td>0.038317</td>\n",
       "      <td>0.039982</td>\n",
       "      <td>-0.072284</td>\n",
       "      <td>-0.089626</td>\n",
       "      <td>-0.002456</td>\n",
       "      <td>-0.125368</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Tunisia</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.054377</td>\n",
       "      <td>-0.113410</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>-0.076653</td>\n",
       "      <td>0.049807</td>\n",
       "      <td>0.065313</td>\n",
       "      <td>0.036596</td>\n",
       "      <td>0.044949</td>\n",
       "      <td>0.056945</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Turkey</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.048857</td>\n",
       "      <td>-0.049216</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>-0.074021</td>\n",
       "      <td>-0.004466</td>\n",
       "      <td>0.026884</td>\n",
       "      <td>0.035387</td>\n",
       "      <td>0.085574</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Turkmenistan</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>-0.047665</td>\n",
       "      <td>0.032306</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>-0.041472</td>\n",
       "      <td>-0.015324</td>\n",
       "      <td>0.062463</td>\n",
       "      <td>-0.039032</td>\n",
       "      <td>-0.003463</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Uganda</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>-0.129614</td>\n",
       "      <td>0.067447</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>0.069437</td>\n",
       "      <td>-0.003569</td>\n",
       "      <td>-0.058889</td>\n",
       "      <td>0.056966</td>\n",
       "      <td>-0.101414</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Ukraine</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>0.067462</td>\n",
       "      <td>-0.023760</td>\n",
       "      <td>-0.083912</td>\n",
       "      <td>0.058952</td>\n",
       "      <td>0.058566</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Uruguay</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.073116</td>\n",
       "      <td>-0.036723</td>\n",
       "      <td>-0.099774</td>\n",
       "      <td>0.038998</td>\n",
       "      <td>0.018240</td>\n",
       "      <td>0.056330</td>\n",
       "      <td>-0.096332</td>\n",
       "      <td>0.068205</td>\n",
       "      <td>-0.098097</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Uzbekistan</th>\n",
       "      <td>0.003479</td>\n",
       "      <td>-0.012733</td>\n",
       "      <td>0.011615</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>-0.064573</td>\n",
       "      <td>-0.054556</td>\n",
       "      <td>0.056301</td>\n",
       "      <td>0.010265</td>\n",
       "      <td>-0.018601</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.007946</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Vanuatu</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>0.022273</td>\n",
       "      <td>-0.016838</td>\n",
       "      <td>-0.144838</td>\n",
       "      <td>-0.088642</td>\n",
       "      <td>0.025231</td>\n",
       "      <td>-0.088404</td>\n",
       "      <td>-0.058267</td>\n",
       "      <td>0.026804</td>\n",
       "      <td>-0.143476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Zambia</th>\n",
       "      <td>0.031720</td>\n",
       "      <td>-0.107050</td>\n",
       "      <td>0.125818</td>\n",
       "      <td>0.065612</td>\n",
       "      <td>-0.046933</td>\n",
       "      <td>-0.010388</td>\n",
       "      <td>-0.010124</td>\n",
       "      <td>0.081803</td>\n",
       "      <td>-0.083966</td>\n",
       "      <td>0.068005</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.006963</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>-0.032394</td>\n",
       "      <td>0.032394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Zimbabwe</th>\n",
       "      <td>-0.019707</td>\n",
       "      <td>-0.216980</td>\n",
       "      <td>0.231425</td>\n",
       "      <td>0.080480</td>\n",
       "      <td>-0.001218</td>\n",
       "      <td>-0.122935</td>\n",
       "      <td>-0.036499</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>-0.065184</td>\n",
       "      <td>0.083416</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.009180</td>\n",
       "      <td>-0.007484</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status_Developed</th>\n",
       "      <td>-0.034138</td>\n",
       "      <td>0.442798</td>\n",
       "      <td>-0.278173</td>\n",
       "      <td>-0.350197</td>\n",
       "      <td>0.607782</td>\n",
       "      <td>0.183455</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.030122</td>\n",
       "      <td>0.298380</td>\n",
       "      <td>-0.357250</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>-0.036969</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>-0.032394</td>\n",
       "      <td>-0.039736</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status_Developing</th>\n",
       "      <td>0.034138</td>\n",
       "      <td>-0.442798</td>\n",
       "      <td>0.278173</td>\n",
       "      <td>0.350197</td>\n",
       "      <td>-0.607782</td>\n",
       "      <td>-0.183455</td>\n",
       "      <td>-0.140351</td>\n",
       "      <td>-0.030122</td>\n",
       "      <td>-0.298380</td>\n",
       "      <td>0.357250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036969</td>\n",
       "      <td>0.036969</td>\n",
       "      <td>0.039736</td>\n",
       "      <td>0.039736</td>\n",
       "      <td>0.036969</td>\n",
       "      <td>0.039736</td>\n",
       "      <td>0.032394</td>\n",
       "      <td>0.039736</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 155 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Year  Life expectancy   Adult Mortality  \\\n",
       "Year                             1.000000          0.050771        -0.037092   \n",
       "Life expectancy                  0.050771          1.000000        -0.702523   \n",
       "Adult Mortality                 -0.037092         -0.702523         1.000000   \n",
       "infant deaths                   -0.001142         -0.443694         0.316530   \n",
       "Alcohol                         -0.113365          0.402718        -0.175535   \n",
       "percentage expenditure           0.110494          0.312599        -0.156047   \n",
       "Hepatitis B                      0.114897          0.199935        -0.105225   \n",
       "Measles                         -0.048340         -0.163407         0.125084   \n",
       " BMI                             0.005739          0.542042        -0.351542   \n",
       "under-five deaths                0.001251         -0.479005         0.348627   \n",
       "Polio                           -0.016699          0.327294        -0.199853   \n",
       "Total expenditure                0.059493          0.174718        -0.085227   \n",
       "Diphtheria                       0.029641          0.341331        -0.191429   \n",
       " HIV/AIDS                       -0.123405         -0.592236         0.550691   \n",
       "GDP                              0.086455          0.471736        -0.260614   \n",
       "Population                       0.012567         -0.022305        -0.015012   \n",
       " thinness  1-19 years            0.019757         -0.457838         0.272230   \n",
       " thinness 5-9 years              0.014122         -0.457508         0.286723   \n",
       "Income composition of resources  0.122892          0.721083        -0.442203   \n",
       "Schooling                        0.088732          0.727630        -0.421171   \n",
       "Country_Afghanistan             -0.008248         -0.125034         0.079685   \n",
       "Country_Albania                 -0.008248          0.065890        -0.097310   \n",
       "Country_Algeria                  0.023252          0.045724        -0.042780   \n",
       "Country_Angola                   0.045440         -0.147892         0.108426   \n",
       "Country_Argentina                0.003479          0.060171        -0.048267   \n",
       "Country_Armenia                 -0.019707          0.043627        -0.038916   \n",
       "Country_Australia               -0.007711          0.132632        -0.078141   \n",
       "Country_Austria                 -0.019707          0.132675        -0.078330   \n",
       "Country_Azerbaijan               0.003479          0.018690        -0.034419   \n",
       "Country_Bangladesh               0.013817          0.006468        -0.022246   \n",
       "...                                   ...               ...              ...   \n",
       "Country_Senegal                  0.023252         -0.051442        -0.001806   \n",
       "Country_Serbia                   0.023252          0.045554        -0.025415   \n",
       "Country_Seychelles              -0.019707          0.032878        -0.005722   \n",
       "Country_Sierra Leone             0.045440         -0.165756         0.093098   \n",
       "Country_Solomon Islands         -0.019707         -0.018401        -0.023415   \n",
       "Country_South Africa            -0.019707         -0.132508         0.191348   \n",
       "Country_Spain                   -0.019707          0.138559        -0.079605   \n",
       "Country_Sri Lanka                0.013817          0.040543        -0.010627   \n",
       "Country_Suriname                 0.031720          0.015434        -0.018902   \n",
       "Country_Swaziland               -0.019707         -0.201364         0.128887   \n",
       "Country_Sweden                   0.056226          0.070639        -0.043975   \n",
       "Country_Syrian Arab Republic    -0.074162          0.030648        -0.021160   \n",
       "Country_Tajikistan               0.003479         -0.024819         0.005539   \n",
       "Country_Thailand                -0.019707          0.039850        -0.005365   \n",
       "Country_Timor-Leste              0.050481         -0.016033        -0.003612   \n",
       "Country_Togo                     0.050481         -0.083278         0.054018   \n",
       "Country_Tonga                   -0.019707          0.034476        -0.029687   \n",
       "Country_Trinidad and Tobago      0.003479          0.020873         0.001051   \n",
       "Country_Tunisia                 -0.019707          0.054377        -0.113410   \n",
       "Country_Turkey                  -0.019707          0.048857        -0.049216   \n",
       "Country_Turkmenistan             0.003479         -0.047665         0.032306   \n",
       "Country_Uganda                   0.003479         -0.129614         0.067447   \n",
       "Country_Ukraine                 -0.019707          0.005931         0.029256   \n",
       "Country_Uruguay                 -0.019707          0.073116        -0.036723   \n",
       "Country_Uzbekistan               0.003479         -0.012733         0.011615   \n",
       "Country_Vanuatu                 -0.019707          0.022273        -0.016838   \n",
       "Country_Zambia                   0.031720         -0.107050         0.125818   \n",
       "Country_Zimbabwe                -0.019707         -0.216980         0.231425   \n",
       "Status_Developed                -0.034138          0.442798        -0.278173   \n",
       "Status_Developing                0.034138         -0.442798         0.278173   \n",
       "\n",
       "                                 infant deaths   Alcohol  \\\n",
       "Year                                 -0.001142 -0.113365   \n",
       "Life expectancy                      -0.443694  0.402718   \n",
       "Adult Mortality                       0.316530 -0.175535   \n",
       "infant deaths                         1.000000 -0.328625   \n",
       "Alcohol                              -0.328625  1.000000   \n",
       "percentage expenditure               -0.206979  0.242047   \n",
       "Hepatitis B                          -0.146108  0.109889   \n",
       "Measles                               0.303586 -0.039255   \n",
       " BMI                                 -0.342299  0.353396   \n",
       "under-five deaths                     0.982329 -0.330412   \n",
       "Polio                                -0.193578  0.240315   \n",
       "Total expenditure                    -0.163771  0.214885   \n",
       "Diphtheria                           -0.152680  0.242951   \n",
       " HIV/AIDS                             0.214648 -0.027113   \n",
       "GDP                                  -0.319806  0.363858   \n",
       "Population                            0.149754 -0.028880   \n",
       " thinness  1-19 years                 0.304793 -0.403755   \n",
       " thinness 5-9 years                   0.307602 -0.386208   \n",
       "Income composition of resources      -0.327805  0.561074   \n",
       "Schooling                            -0.425840  0.616975   \n",
       "Country_Afghanistan                   0.083145 -0.111047   \n",
       "Country_Albania                      -0.096288  0.007755   \n",
       "Country_Algeria                       0.068835 -0.083127   \n",
       "Country_Angola                        0.058649  0.053507   \n",
       "Country_Argentina                     0.074877  0.076825   \n",
       "Country_Armenia                      -0.069732 -0.019756   \n",
       "Country_Australia                    -0.067346  0.129134   \n",
       "Country_Austria                      -0.144838  0.183224   \n",
       "Country_Azerbaijan                    0.074877 -0.076966   \n",
       "Country_Bangladesh                    0.071918 -0.096145   \n",
       "...                                        ...       ...   \n",
       "Country_Senegal                       0.068835 -0.086087   \n",
       "Country_Serbia                       -0.059642  0.094816   \n",
       "Country_Seychelles                   -0.144838  0.039679   \n",
       "Country_Sierra Leone                  0.058649 -0.036566   \n",
       "Country_Solomon Islands              -0.144838 -0.088784   \n",
       "Country_South Africa                  0.080480  0.072679   \n",
       "Country_Spain                        -0.019661  0.130608   \n",
       "Country_Sri Lanka                     0.071918 -0.049081   \n",
       "Country_Suriname                     -0.118079  0.014385   \n",
       "Country_Swaziland                     0.055445 -0.001662   \n",
       "Country_Sweden                       -0.074543  0.034484   \n",
       "Country_Syrian Arab Republic          0.058649 -0.059317   \n",
       "Country_Tajikistan                    0.074877 -0.093973   \n",
       "Country_Thailand                      0.080480  0.038014   \n",
       "Country_Timor-Leste                   0.003663 -0.071676   \n",
       "Country_Togo                          0.054845 -0.061325   \n",
       "Country_Tonga                        -0.144838 -0.080063   \n",
       "Country_Trinidad and Tobago          -0.134754  0.038317   \n",
       "Country_Tunisia                       0.080480 -0.076653   \n",
       "Country_Turkey                        0.080480 -0.074021   \n",
       "Country_Turkmenistan                  0.074877 -0.041472   \n",
       "Country_Uganda                        0.074877  0.069437   \n",
       "Country_Ukraine                       0.080480  0.067462   \n",
       "Country_Uruguay                      -0.099774  0.038998   \n",
       "Country_Uzbekistan                    0.074877 -0.064573   \n",
       "Country_Vanuatu                      -0.144838 -0.088642   \n",
       "Country_Zambia                        0.065612 -0.046933   \n",
       "Country_Zimbabwe                      0.080480 -0.001218   \n",
       "Status_Developed                     -0.350197  0.607782   \n",
       "Status_Developing                     0.350197 -0.607782   \n",
       "\n",
       "                                 percentage expenditure  Hepatitis B  \\\n",
       "Year                                           0.110494     0.114897   \n",
       "Life expectancy                                0.312599     0.199935   \n",
       "Adult Mortality                               -0.156047    -0.105225   \n",
       "infant deaths                                 -0.206979    -0.146108   \n",
       "Alcohol                                        0.242047     0.109889   \n",
       "percentage expenditure                         1.000000     0.110430   \n",
       "Hepatitis B                                    0.110430     1.000000   \n",
       "Measles                                       -0.098559    -0.093302   \n",
       " BMI                                           0.246558     0.143302   \n",
       "under-five deaths                             -0.211914    -0.140176   \n",
       "Polio                                          0.122143     0.463331   \n",
       "Total expenditure                              0.146727     0.113327   \n",
       "Diphtheria                                     0.151581     0.588990   \n",
       " HIV/AIDS                                     -0.112271    -0.094802   \n",
       "GDP                                            0.711232     0.149114   \n",
       "Population                                    -0.027590    -0.129723   \n",
       " thinness  1-19 years                         -0.197801    -0.129406   \n",
       " thinness 5-9 years                           -0.209594    -0.133251   \n",
       "Income composition of resources                0.326441     0.184921   \n",
       "Schooling                                      0.351102     0.215182   \n",
       "Country_Afghanistan                           -0.071252    -0.056672   \n",
       "Country_Albania                                0.008594     0.072632   \n",
       "Country_Algeria                                0.015306    -0.008846   \n",
       "Country_Angola                                 0.012702    -0.022416   \n",
       "Country_Argentina                              0.056327     0.003796   \n",
       "Country_Armenia                               -0.002199     0.029629   \n",
       "Country_Australia                              0.046006     0.051373   \n",
       "Country_Austria                                0.061377     0.003926   \n",
       "Country_Azerbaijan                             0.011314    -0.079517   \n",
       "Country_Bangladesh                            -0.034271    -0.010763   \n",
       "...                                                 ...          ...   \n",
       "Country_Senegal                               -0.111034     0.022296   \n",
       "Country_Serbia                                 0.037482     0.035975   \n",
       "Country_Seychelles                             0.016764     0.073049   \n",
       "Country_Sierra Leone                          -0.073118     0.010658   \n",
       "Country_Solomon Islands                       -0.046464    -0.007053   \n",
       "Country_South Africa                           0.047879    -0.049725   \n",
       "Country_Spain                                  0.047071     0.048594   \n",
       "Country_Sri Lanka                             -0.025858     0.035787   \n",
       "Country_Suriname                               0.057221     0.017340   \n",
       "Country_Swaziland                              0.005213     0.004675   \n",
       "Country_Sweden                                 0.027288    -0.042320   \n",
       "Country_Syrian Arab Republic                   0.038852    -0.043215   \n",
       "Country_Tajikistan                            -0.119054     0.015047   \n",
       "Country_Thailand                               0.027188     0.066062   \n",
       "Country_Timor-Leste                           -0.074309    -0.008208   \n",
       "Country_Togo                                  -0.016050    -0.010758   \n",
       "Country_Tonga                                  0.008869    -0.014540   \n",
       "Country_Trinidad and Tobago                    0.039982    -0.072284   \n",
       "Country_Tunisia                                0.049807     0.065313   \n",
       "Country_Turkey                                -0.004466     0.026884   \n",
       "Country_Turkmenistan                          -0.015324     0.062463   \n",
       "Country_Uganda                                -0.003569    -0.058889   \n",
       "Country_Ukraine                               -0.023760    -0.083912   \n",
       "Country_Uruguay                                0.018240     0.056330   \n",
       "Country_Uzbekistan                            -0.054556     0.056301   \n",
       "Country_Vanuatu                                0.025231    -0.088404   \n",
       "Country_Zambia                                -0.010388    -0.010124   \n",
       "Country_Zimbabwe                              -0.122935    -0.036499   \n",
       "Status_Developed                               0.183455     0.140351   \n",
       "Status_Developing                             -0.183455    -0.140351   \n",
       "\n",
       "                                 Measles       BMI   under-five deaths   \\\n",
       "Year                            -0.048340  0.005739            0.001251   \n",
       "Life expectancy                 -0.163407  0.542042           -0.479005   \n",
       "Adult Mortality                  0.125084 -0.351542            0.348627   \n",
       "infant deaths                    0.303586 -0.342299            0.982329   \n",
       "Alcohol                         -0.039255  0.353396           -0.330412   \n",
       "percentage expenditure          -0.098559  0.246558           -0.211914   \n",
       "Hepatitis B                     -0.093302  0.143302           -0.140176   \n",
       "Measles                          1.000000 -0.155825            0.303885   \n",
       " BMI                            -0.155825  1.000000           -0.362092   \n",
       "under-five deaths                0.303885 -0.362092            1.000000   \n",
       "Polio                           -0.082422  0.186268           -0.191011   \n",
       "Total expenditure               -0.076945  0.189469           -0.175422   \n",
       "Diphtheria                      -0.096865  0.176295           -0.153218   \n",
       " HIV/AIDS                        0.097443 -0.210897            0.243313   \n",
       "GDP                             -0.116230  0.382356           -0.326580   \n",
       "Population                       0.087762 -0.081416            0.150614   \n",
       " thinness  1-19 years            0.230897 -0.547018            0.324214   \n",
       " thinness 5-9 years              0.240425 -0.554094            0.326861   \n",
       "Income composition of resources -0.113389  0.510505           -0.342213   \n",
       "Schooling                       -0.115381  0.554844           -0.448940   \n",
       "Country_Afghanistan              0.059655 -0.113328            0.086178   \n",
       "Country_Albania                 -0.007059  0.054835           -0.093288   \n",
       "Country_Algeria                  0.031770  0.044584            0.071346   \n",
       "Country_Angola                   0.042080 -0.069576            0.060788   \n",
       "Country_Argentina               -0.072761  0.073829            0.077609   \n",
       "Country_Armenia                 -0.003887  0.028615           -0.086753   \n",
       "Country_Australia                0.046390  0.078720           -0.064220   \n",
       "Country_Austria                  0.046867  0.046275           -0.143476   \n",
       "Country_Azerbaijan              -0.020221  0.023829            0.077609   \n",
       "Country_Bangladesh               0.051600 -0.102694            0.074541   \n",
       "...                                   ...       ...                 ...   \n",
       "Country_Senegal                  0.057140 -0.083376            0.071346   \n",
       "Country_Serbia                  -0.004875  0.075782           -0.074200   \n",
       "Country_Seychelles              -0.089686 -0.050079           -0.143476   \n",
       "Country_Sierra Leone             0.060243 -0.073023            0.060788   \n",
       "Country_Solomon Islands         -0.096332  0.015969           -0.143476   \n",
       "Country_South Africa             0.093392 -0.004085            0.083416   \n",
       "Country_Spain                    0.070432  0.097088           -0.033811   \n",
       "Country_Sri Lanka                0.074547 -0.089724            0.066093   \n",
       "Country_Suriname                -0.078535  0.064438           -0.116968   \n",
       "Country_Swaziland               -0.001470 -0.063469            0.068290   \n",
       "Country_Sweden                   0.065869  0.050430           -0.073842   \n",
       "Country_Syrian Arab Republic     0.042080  0.029642            0.060788   \n",
       "Country_Tajikistan              -0.050059 -0.011622            0.077609   \n",
       "Country_Thailand                 0.047472 -0.084105            0.083416   \n",
       "Country_Timor-Leste              0.061408 -0.073682            0.001624   \n",
       "Country_Togo                     0.039350 -0.070281            0.056845   \n",
       "Country_Tonga                   -0.093915  0.116430           -0.143476   \n",
       "Country_Trinidad and Tobago     -0.089626 -0.002456           -0.125368   \n",
       "Country_Tunisia                  0.036596  0.044949            0.056945   \n",
       "Country_Turkey                   0.035387  0.085574            0.083416   \n",
       "Country_Turkmenistan            -0.039032 -0.003463            0.077609   \n",
       "Country_Uganda                   0.056966 -0.101414            0.077609   \n",
       "Country_Ukraine                  0.058952  0.058566            0.083416   \n",
       "Country_Uruguay                 -0.096332  0.068205           -0.098097   \n",
       "Country_Uzbekistan               0.010265 -0.018601            0.077609   \n",
       "Country_Vanuatu                 -0.058267  0.026804           -0.143476   \n",
       "Country_Zambia                   0.081803 -0.083966            0.068005   \n",
       "Country_Zimbabwe                 0.014844 -0.065184            0.083416   \n",
       "Status_Developed                 0.030122  0.298380           -0.357250   \n",
       "Status_Developing               -0.030122 -0.298380            0.357250   \n",
       "\n",
       "                                       ...          Country_Turkmenistan  \\\n",
       "Year                                   ...                      0.003479   \n",
       "Life expectancy                        ...                     -0.047665   \n",
       "Adult Mortality                        ...                      0.032306   \n",
       "infant deaths                          ...                      0.074877   \n",
       "Alcohol                                ...                     -0.041472   \n",
       "percentage expenditure                 ...                     -0.015324   \n",
       "Hepatitis B                            ...                      0.062463   \n",
       "Measles                                ...                     -0.039032   \n",
       " BMI                                   ...                     -0.003463   \n",
       "under-five deaths                      ...                      0.077609   \n",
       "Polio                                  ...                      0.046335   \n",
       "Total expenditure                      ...                     -0.127694   \n",
       "Diphtheria                             ...                      0.049898   \n",
       " HIV/AIDS                              ...                     -0.027847   \n",
       "GDP                                    ...                     -0.007515   \n",
       "Population                             ...                     -0.015425   \n",
       " thinness  1-19 years                  ...                     -0.030063   \n",
       " thinness 5-9 years                    ...                     -0.030069   \n",
       "Income composition of resources        ...                     -0.206504   \n",
       "Schooling                              ...                     -0.052163   \n",
       "Country_Afghanistan                    ...                     -0.008824   \n",
       "Country_Albania                        ...                     -0.008824   \n",
       "Country_Algeria                        ...                     -0.007305   \n",
       "Country_Angola                         ...                     -0.006224   \n",
       "Country_Argentina                      ...                     -0.007946   \n",
       "Country_Armenia                        ...                     -0.008541   \n",
       "Country_Australia                      ...                     -0.008249   \n",
       "Country_Austria                        ...                     -0.008541   \n",
       "Country_Azerbaijan                     ...                     -0.007946   \n",
       "Country_Bangladesh                     ...                     -0.007632   \n",
       "...                                    ...                           ...   \n",
       "Country_Senegal                        ...                     -0.007305   \n",
       "Country_Serbia                         ...                     -0.007305   \n",
       "Country_Seychelles                     ...                     -0.008541   \n",
       "Country_Sierra Leone                   ...                     -0.006224   \n",
       "Country_Solomon Islands                ...                     -0.008541   \n",
       "Country_South Africa                   ...                     -0.008541   \n",
       "Country_Spain                          ...                     -0.008541   \n",
       "Country_Sri Lanka                      ...                     -0.007632   \n",
       "Country_Suriname                       ...                     -0.006963   \n",
       "Country_Swaziland                      ...                     -0.008541   \n",
       "Country_Sweden                         ...                     -0.004396   \n",
       "Country_Syrian Arab Republic           ...                     -0.006224   \n",
       "Country_Tajikistan                     ...                     -0.007946   \n",
       "Country_Thailand                       ...                     -0.008541   \n",
       "Country_Timor-Leste                    ...                     -0.005820   \n",
       "Country_Togo                           ...                     -0.005820   \n",
       "Country_Tonga                          ...                     -0.008541   \n",
       "Country_Trinidad and Tobago            ...                     -0.007946   \n",
       "Country_Tunisia                        ...                     -0.008541   \n",
       "Country_Turkey                         ...                     -0.008541   \n",
       "Country_Turkmenistan                   ...                      1.000000   \n",
       "Country_Uganda                         ...                     -0.007946   \n",
       "Country_Ukraine                        ...                     -0.008541   \n",
       "Country_Uruguay                        ...                     -0.008541   \n",
       "Country_Uzbekistan                     ...                     -0.007946   \n",
       "Country_Vanuatu                        ...                     -0.008541   \n",
       "Country_Zambia                         ...                     -0.006963   \n",
       "Country_Zimbabwe                       ...                     -0.008541   \n",
       "Status_Developed                       ...                     -0.036969   \n",
       "Status_Developing                      ...                      0.036969   \n",
       "\n",
       "                                 Country_Uganda  Country_Ukraine  \\\n",
       "Year                                   0.003479        -0.019707   \n",
       "Life expectancy                       -0.129614         0.005931   \n",
       "Adult Mortality                        0.067447         0.029256   \n",
       "infant deaths                          0.074877         0.080480   \n",
       "Alcohol                                0.069437         0.067462   \n",
       "percentage expenditure                -0.003569        -0.023760   \n",
       "Hepatitis B                           -0.058889        -0.083912   \n",
       "Measles                                0.056966         0.058952   \n",
       " BMI                                  -0.101414         0.058566   \n",
       "under-five deaths                      0.077609         0.083416   \n",
       "Polio                                 -0.070985        -0.000418   \n",
       "Total expenditure                      0.109396         0.034237   \n",
       "Diphtheria                            -0.076610        -0.048952   \n",
       " HIV/AIDS                              0.080856        -0.022622   \n",
       "GDP                                   -0.097368        -0.024721   \n",
       "Population                             0.002964        -0.005825   \n",
       " thinness  1-19 years                  0.026310        -0.047039   \n",
       " thinness 5-9 years                    0.024760        -0.045605   \n",
       "Income composition of resources       -0.088083         0.043404   \n",
       "Schooling                             -0.045783         0.083660   \n",
       "Country_Afghanistan                   -0.008824        -0.009484   \n",
       "Country_Albania                       -0.008824        -0.009484   \n",
       "Country_Algeria                       -0.007305        -0.007852   \n",
       "Country_Angola                        -0.006224        -0.006690   \n",
       "Country_Argentina                     -0.007946        -0.008541   \n",
       "Country_Armenia                       -0.008541        -0.009180   \n",
       "Country_Australia                     -0.008249        -0.008866   \n",
       "Country_Austria                       -0.008541        -0.009180   \n",
       "Country_Azerbaijan                    -0.007946        -0.008541   \n",
       "Country_Bangladesh                    -0.007632        -0.008203   \n",
       "...                                         ...              ...   \n",
       "Country_Senegal                       -0.007305        -0.007852   \n",
       "Country_Serbia                        -0.007305        -0.007852   \n",
       "Country_Seychelles                    -0.008541        -0.009180   \n",
       "Country_Sierra Leone                  -0.006224        -0.006690   \n",
       "Country_Solomon Islands               -0.008541        -0.009180   \n",
       "Country_South Africa                  -0.008541        -0.009180   \n",
       "Country_Spain                         -0.008541        -0.009180   \n",
       "Country_Sri Lanka                     -0.007632        -0.008203   \n",
       "Country_Suriname                      -0.006963        -0.007484   \n",
       "Country_Swaziland                     -0.008541        -0.009180   \n",
       "Country_Sweden                        -0.004396        -0.004725   \n",
       "Country_Syrian Arab Republic          -0.006224        -0.006690   \n",
       "Country_Tajikistan                    -0.007946        -0.008541   \n",
       "Country_Thailand                      -0.008541        -0.009180   \n",
       "Country_Timor-Leste                   -0.005820        -0.006256   \n",
       "Country_Togo                          -0.005820        -0.006256   \n",
       "Country_Tonga                         -0.008541        -0.009180   \n",
       "Country_Trinidad and Tobago           -0.007946        -0.008541   \n",
       "Country_Tunisia                       -0.008541        -0.009180   \n",
       "Country_Turkey                        -0.008541        -0.009180   \n",
       "Country_Turkmenistan                  -0.007946        -0.008541   \n",
       "Country_Uganda                         1.000000        -0.008541   \n",
       "Country_Ukraine                       -0.008541         1.000000   \n",
       "Country_Uruguay                       -0.008541        -0.009180   \n",
       "Country_Uzbekistan                    -0.007946        -0.008541   \n",
       "Country_Vanuatu                       -0.008541        -0.009180   \n",
       "Country_Zambia                        -0.006963        -0.007484   \n",
       "Country_Zimbabwe                      -0.008541        -0.009180   \n",
       "Status_Developed                      -0.036969        -0.039736   \n",
       "Status_Developing                      0.036969         0.039736   \n",
       "\n",
       "                                 Country_Uruguay  Country_Uzbekistan  \\\n",
       "Year                                   -0.019707            0.003479   \n",
       "Life expectancy                         0.073116           -0.012733   \n",
       "Adult Mortality                        -0.036723            0.011615   \n",
       "infant deaths                          -0.099774            0.074877   \n",
       "Alcohol                                 0.038998           -0.064573   \n",
       "percentage expenditure                  0.018240           -0.054556   \n",
       "Hepatitis B                             0.056330            0.056301   \n",
       "Measles                                -0.096332            0.010265   \n",
       " BMI                                    0.068205           -0.018601   \n",
       "under-five deaths                      -0.098097            0.077609   \n",
       "Polio                                   0.045402            0.059167   \n",
       "Total expenditure                       0.116460           -0.010223   \n",
       "Diphtheria                              0.020333            0.058480   \n",
       " HIV/AIDS                              -0.029931           -0.026028   \n",
       "GDP                                     0.032108           -0.080485   \n",
       "Population                             -0.016766           -0.017413   \n",
       " thinness  1-19 years                  -0.067599           -0.033643   \n",
       " thinness 5-9 years                    -0.069082           -0.033606   \n",
       "Income composition of resources         0.069193            0.005501   \n",
       "Schooling                               0.106060           -0.011431   \n",
       "Country_Afghanistan                    -0.009484           -0.008824   \n",
       "Country_Albania                        -0.009484           -0.008824   \n",
       "Country_Algeria                        -0.007852           -0.007305   \n",
       "Country_Angola                         -0.006690           -0.006224   \n",
       "Country_Argentina                      -0.008541           -0.007946   \n",
       "Country_Armenia                        -0.009180           -0.008541   \n",
       "Country_Australia                      -0.008866           -0.008249   \n",
       "Country_Austria                        -0.009180           -0.008541   \n",
       "Country_Azerbaijan                     -0.008541           -0.007946   \n",
       "Country_Bangladesh                     -0.008203           -0.007632   \n",
       "...                                          ...                 ...   \n",
       "Country_Senegal                        -0.007852           -0.007305   \n",
       "Country_Serbia                         -0.007852           -0.007305   \n",
       "Country_Seychelles                     -0.009180           -0.008541   \n",
       "Country_Sierra Leone                   -0.006690           -0.006224   \n",
       "Country_Solomon Islands                -0.009180           -0.008541   \n",
       "Country_South Africa                   -0.009180           -0.008541   \n",
       "Country_Spain                          -0.009180           -0.008541   \n",
       "Country_Sri Lanka                      -0.008203           -0.007632   \n",
       "Country_Suriname                       -0.007484           -0.006963   \n",
       "Country_Swaziland                      -0.009180           -0.008541   \n",
       "Country_Sweden                         -0.004725           -0.004396   \n",
       "Country_Syrian Arab Republic           -0.006690           -0.006224   \n",
       "Country_Tajikistan                     -0.008541           -0.007946   \n",
       "Country_Thailand                       -0.009180           -0.008541   \n",
       "Country_Timor-Leste                    -0.006256           -0.005820   \n",
       "Country_Togo                           -0.006256           -0.005820   \n",
       "Country_Tonga                          -0.009180           -0.008541   \n",
       "Country_Trinidad and Tobago            -0.008541           -0.007946   \n",
       "Country_Tunisia                        -0.009180           -0.008541   \n",
       "Country_Turkey                         -0.009180           -0.008541   \n",
       "Country_Turkmenistan                   -0.008541           -0.007946   \n",
       "Country_Uganda                         -0.008541           -0.007946   \n",
       "Country_Ukraine                        -0.009180           -0.008541   \n",
       "Country_Uruguay                         1.000000           -0.008541   \n",
       "Country_Uzbekistan                     -0.008541            1.000000   \n",
       "Country_Vanuatu                        -0.009180           -0.008541   \n",
       "Country_Zambia                         -0.007484           -0.006963   \n",
       "Country_Zimbabwe                       -0.009180           -0.008541   \n",
       "Status_Developed                       -0.039736           -0.036969   \n",
       "Status_Developing                       0.039736            0.036969   \n",
       "\n",
       "                                 Country_Vanuatu  Country_Zambia  \\\n",
       "Year                                   -0.019707        0.031720   \n",
       "Life expectancy                         0.022273       -0.107050   \n",
       "Adult Mortality                        -0.016838        0.125818   \n",
       "infant deaths                          -0.144838        0.065612   \n",
       "Alcohol                                -0.088642       -0.046933   \n",
       "percentage expenditure                  0.025231       -0.010388   \n",
       "Hepatitis B                            -0.088404       -0.010124   \n",
       "Measles                                -0.058267        0.081803   \n",
       " BMI                                    0.026804       -0.083966   \n",
       "under-five deaths                      -0.143476        0.068005   \n",
       "Polio                                  -0.073844       -0.083404   \n",
       "Total expenditure                      -0.084498       -0.028405   \n",
       "Diphtheria                             -0.112908       -0.029891   \n",
       " HIV/AIDS                              -0.029931        0.097742   \n",
       "GDP                                     0.047822       -0.019979   \n",
       "Population                             -0.019756       -0.008112   \n",
       " thinness  1-19 years                  -0.068294        0.031418   \n",
       " thinness 5-9 years                    -0.070181        0.027740   \n",
       "Income composition of resources        -0.146266       -0.046538   \n",
       "Schooling                              -0.053710       -0.012295   \n",
       "Country_Afghanistan                    -0.009484       -0.007732   \n",
       "Country_Albania                        -0.009484       -0.007732   \n",
       "Country_Algeria                        -0.007852       -0.006401   \n",
       "Country_Angola                         -0.006690       -0.005454   \n",
       "Country_Argentina                      -0.008541       -0.006963   \n",
       "Country_Armenia                        -0.009180       -0.007484   \n",
       "Country_Australia                      -0.008866       -0.007228   \n",
       "Country_Austria                        -0.009180       -0.007484   \n",
       "Country_Azerbaijan                     -0.008541       -0.006963   \n",
       "Country_Bangladesh                     -0.008203       -0.006688   \n",
       "...                                          ...             ...   \n",
       "Country_Senegal                        -0.007852       -0.006401   \n",
       "Country_Serbia                         -0.007852       -0.006401   \n",
       "Country_Seychelles                     -0.009180       -0.007484   \n",
       "Country_Sierra Leone                   -0.006690       -0.005454   \n",
       "Country_Solomon Islands                -0.009180       -0.007484   \n",
       "Country_South Africa                   -0.009180       -0.007484   \n",
       "Country_Spain                          -0.009180       -0.007484   \n",
       "Country_Sri Lanka                      -0.008203       -0.006688   \n",
       "Country_Suriname                       -0.007484       -0.006101   \n",
       "Country_Swaziland                      -0.009180       -0.007484   \n",
       "Country_Sweden                         -0.004725       -0.003852   \n",
       "Country_Syrian Arab Republic           -0.006690       -0.005454   \n",
       "Country_Tajikistan                     -0.008541       -0.006963   \n",
       "Country_Thailand                       -0.009180       -0.007484   \n",
       "Country_Timor-Leste                    -0.006256       -0.005100   \n",
       "Country_Togo                           -0.006256       -0.005100   \n",
       "Country_Tonga                          -0.009180       -0.007484   \n",
       "Country_Trinidad and Tobago            -0.008541       -0.006963   \n",
       "Country_Tunisia                        -0.009180       -0.007484   \n",
       "Country_Turkey                         -0.009180       -0.007484   \n",
       "Country_Turkmenistan                   -0.008541       -0.006963   \n",
       "Country_Uganda                         -0.008541       -0.006963   \n",
       "Country_Ukraine                        -0.009180       -0.007484   \n",
       "Country_Uruguay                        -0.009180       -0.007484   \n",
       "Country_Uzbekistan                     -0.008541       -0.006963   \n",
       "Country_Vanuatu                         1.000000       -0.007484   \n",
       "Country_Zambia                         -0.007484        1.000000   \n",
       "Country_Zimbabwe                       -0.009180       -0.007484   \n",
       "Status_Developed                       -0.039736       -0.032394   \n",
       "Status_Developing                       0.039736        0.032394   \n",
       "\n",
       "                                 Country_Zimbabwe  Status_Developed  \\\n",
       "Year                                    -0.019707         -0.034138   \n",
       "Life expectancy                         -0.216980          0.442798   \n",
       "Adult Mortality                          0.231425         -0.278173   \n",
       "infant deaths                            0.080480         -0.350197   \n",
       "Alcohol                                 -0.001218          0.607782   \n",
       "percentage expenditure                  -0.122935          0.183455   \n",
       "Hepatitis B                             -0.036499          0.140351   \n",
       "Measles                                  0.014844          0.030122   \n",
       " BMI                                    -0.065184          0.298380   \n",
       "under-five deaths                        0.083416         -0.357250   \n",
       "Polio                                   -0.037416          0.201917   \n",
       "Total expenditure                        0.008451          0.192538   \n",
       "Diphtheria                              -0.043327          0.201654   \n",
       " HIV/AIDS                                0.356143         -0.129555   \n",
       "GDP                                     -0.104169          0.277297   \n",
       "Population                              -0.009725         -0.034790   \n",
       " thinness  1-19 years                    0.047012         -0.308005   \n",
       " thinness 5-9 years                      0.044873         -0.307279   \n",
       "Income composition of resources         -0.103098          0.463615   \n",
       "Schooling                               -0.079767          0.512543   \n",
       "Country_Afghanistan                     -0.009484         -0.041051   \n",
       "Country_Albania                         -0.009484         -0.041051   \n",
       "Country_Algeria                         -0.007852         -0.033986   \n",
       "Country_Angola                          -0.006690         -0.028957   \n",
       "Country_Argentina                       -0.008541         -0.036969   \n",
       "Country_Armenia                         -0.009180         -0.039736   \n",
       "Country_Australia                       -0.008866          0.223123   \n",
       "Country_Austria                         -0.009180          0.231025   \n",
       "Country_Azerbaijan                      -0.008541         -0.036969   \n",
       "Country_Bangladesh                      -0.008203         -0.035508   \n",
       "...                                           ...               ...   \n",
       "Country_Senegal                         -0.007852         -0.033986   \n",
       "Country_Serbia                          -0.007852         -0.033986   \n",
       "Country_Seychelles                      -0.009180         -0.039736   \n",
       "Country_Sierra Leone                    -0.006690         -0.028957   \n",
       "Country_Solomon Islands                 -0.009180         -0.039736   \n",
       "Country_South Africa                    -0.009180         -0.039736   \n",
       "Country_Spain                           -0.009180          0.231025   \n",
       "Country_Sri Lanka                       -0.008203         -0.035508   \n",
       "Country_Suriname                        -0.007484         -0.032394   \n",
       "Country_Swaziland                       -0.009180         -0.039736   \n",
       "Country_Sweden                          -0.004725          0.118901   \n",
       "Country_Syrian Arab Republic            -0.006690         -0.028957   \n",
       "Country_Tajikistan                      -0.008541         -0.036969   \n",
       "Country_Thailand                        -0.009180         -0.039736   \n",
       "Country_Timor-Leste                     -0.006256         -0.027078   \n",
       "Country_Togo                            -0.006256         -0.027078   \n",
       "Country_Tonga                           -0.009180         -0.039736   \n",
       "Country_Trinidad and Tobago             -0.008541         -0.036969   \n",
       "Country_Tunisia                         -0.009180         -0.039736   \n",
       "Country_Turkey                          -0.009180         -0.039736   \n",
       "Country_Turkmenistan                    -0.008541         -0.036969   \n",
       "Country_Uganda                          -0.008541         -0.036969   \n",
       "Country_Ukraine                         -0.009180         -0.039736   \n",
       "Country_Uruguay                         -0.009180         -0.039736   \n",
       "Country_Uzbekistan                      -0.008541         -0.036969   \n",
       "Country_Vanuatu                         -0.009180         -0.039736   \n",
       "Country_Zambia                          -0.007484         -0.032394   \n",
       "Country_Zimbabwe                         1.000000         -0.039736   \n",
       "Status_Developed                        -0.039736          1.000000   \n",
       "Status_Developing                        0.039736         -1.000000   \n",
       "\n",
       "                                 Status_Developing  \n",
       "Year                                      0.034138  \n",
       "Life expectancy                          -0.442798  \n",
       "Adult Mortality                           0.278173  \n",
       "infant deaths                             0.350197  \n",
       "Alcohol                                  -0.607782  \n",
       "percentage expenditure                   -0.183455  \n",
       "Hepatitis B                              -0.140351  \n",
       "Measles                                  -0.030122  \n",
       " BMI                                     -0.298380  \n",
       "under-five deaths                         0.357250  \n",
       "Polio                                    -0.201917  \n",
       "Total expenditure                        -0.192538  \n",
       "Diphtheria                               -0.201654  \n",
       " HIV/AIDS                                 0.129555  \n",
       "GDP                                      -0.277297  \n",
       "Population                                0.034790  \n",
       " thinness  1-19 years                     0.308005  \n",
       " thinness 5-9 years                       0.307279  \n",
       "Income composition of resources          -0.463615  \n",
       "Schooling                                -0.512543  \n",
       "Country_Afghanistan                       0.041051  \n",
       "Country_Albania                           0.041051  \n",
       "Country_Algeria                           0.033986  \n",
       "Country_Angola                            0.028957  \n",
       "Country_Argentina                         0.036969  \n",
       "Country_Armenia                           0.039736  \n",
       "Country_Australia                        -0.223123  \n",
       "Country_Austria                          -0.231025  \n",
       "Country_Azerbaijan                        0.036969  \n",
       "Country_Bangladesh                        0.035508  \n",
       "...                                            ...  \n",
       "Country_Senegal                           0.033986  \n",
       "Country_Serbia                            0.033986  \n",
       "Country_Seychelles                        0.039736  \n",
       "Country_Sierra Leone                      0.028957  \n",
       "Country_Solomon Islands                   0.039736  \n",
       "Country_South Africa                      0.039736  \n",
       "Country_Spain                            -0.231025  \n",
       "Country_Sri Lanka                         0.035508  \n",
       "Country_Suriname                          0.032394  \n",
       "Country_Swaziland                         0.039736  \n",
       "Country_Sweden                           -0.118901  \n",
       "Country_Syrian Arab Republic              0.028957  \n",
       "Country_Tajikistan                        0.036969  \n",
       "Country_Thailand                          0.039736  \n",
       "Country_Timor-Leste                       0.027078  \n",
       "Country_Togo                              0.027078  \n",
       "Country_Tonga                             0.039736  \n",
       "Country_Trinidad and Tobago               0.036969  \n",
       "Country_Tunisia                           0.039736  \n",
       "Country_Turkey                            0.039736  \n",
       "Country_Turkmenistan                      0.036969  \n",
       "Country_Uganda                            0.036969  \n",
       "Country_Ukraine                           0.039736  \n",
       "Country_Uruguay                           0.039736  \n",
       "Country_Uzbekistan                        0.036969  \n",
       "Country_Vanuatu                           0.039736  \n",
       "Country_Zambia                            0.032394  \n",
       "Country_Zimbabwe                          0.039736  \n",
       "Status_Developed                         -1.000000  \n",
       "Status_Developing                         1.000000  \n",
       "\n",
       "[155 rows x 155 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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